Stability-integrated Fuzzy C means segmentation for spatial ...

Indian Academy of Sciences (India)

V ROYNA DAISY

2018-03-16

Mar 16, 2018 ... clusters and including spatial information to basic Fuzzy C Means clustering are done in .... modify the objective function with Kernel distance function .... spatial information, thus making it sensitive to noise and outliers.

Land cover classification using reformed fuzzy C-means

Indian Academy of Sciences (India)

This paper explains the task of land cover classification using reformed fuzzy C means. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. The most basic attribute for clustering of an image is its luminance ...

Soft Sensor Modeling Based on Multiple Gaussian Process Regression and Fuzzy C-mean Clustering

Directory of Open Access Journals (Sweden)

Xianglin ZHU

2014-06-01

Full Text Available In order to overcome the difficulties of online measurement of some crucial biochemical variables in fermentation processes, a new soft sensor modeling method is presented based on the Gaussian process regression and fuzzy C-mean clustering. With the consideration that the typical fermentation process can be distributed into 4 phases including lag phase, exponential growth phase, stable phase and dead phase, the training samples are classified into 4 subcategories by using fuzzy C- mean clustering algorithm. For each sub-category, the samples are trained using the Gaussian process regression and the corresponding soft-sensing sub-model is established respectively. For a new sample, the membership between this sample and sub-models are computed based on the Euclidean distance, and then the prediction output of soft sensor is obtained using the weighting sum. Taking the Lysine fermentation as example, the simulation and experiment are carried out and the corresponding results show that the presented method achieves better fitting and generalization ability than radial basis function neutral network and single Gaussian process regression model.

Fuzzy Clustering

DEFF Research Database (Denmark)

Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan

2000-01-01

A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...

Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment

Science.gov (United States)

Chen, Xiao; Li, Yaan; Yu, Jing; Li, Yuxing

2018-01-01

For fast and more effective implementation of tracking multiple targets in a cluttered environment, we propose a multiple targets tracking (MTT) algorithm called maximum entropy fuzzy c-means clustering joint probabilistic data association that combines fuzzy c-means clustering and the joint probabilistic data association (PDA) algorithm. The algorithm uses the membership value to express the probability of the target originating from measurement. The membership value is obtained through fuzzy c-means clustering objective function optimized by the maximum entropy principle. When considering the effect of the public measurement, we use a correction factor to adjust the association probability matrix to estimate the state of the target. As this algorithm avoids confirmation matrix splitting, it can solve the high computational load problem of the joint PDA algorithm. The results of simulations and analysis conducted for tracking neighbor parallel targets and cross targets in a different density cluttered environment show that the proposed algorithm can realize MTT quickly and efficiently in a cluttered environment. Further, the performance of the proposed algorithm remains constant with increasing process noise variance. The proposed algorithm has the advantages of efficiency and low computational load, which can ensure optimum performance when tracking multiple targets in a dense cluttered environment.

Approximate fuzzy C-means (AFCM) cluster analysis of medical magnetic resonance image (MRI) data

International Nuclear Information System (INIS)

DelaPaz, R.L.; Chang, P.J.; Bernstein, R.; Dave, J.V.

1987-01-01

The authors describe the application of an approximate fuzzy C-means (AFCM) clustering algorithm as a data dimension reduction approach to medical magnetic resonance images (MRI). Image data consisted of one T1-weighted, two T2-weighted, and one T2*-weighted (magnetic susceptibility) image for each cranial study and a matrix of 10 images generated from 10 combinations of TE and TR for each body lymphoma study. All images were obtained with a 1.5 Tesla imaging system (GE Signa). Analyses were performed on over 100 MR image sets with a variety of pathologies. The cluster analysis was operated in an unsupervised mode and computational overhead was minimized by utilizing a table look-up approach without adversely affecting accuracy. Image data were first segmented into 2 coarse clusters, each of which was then subdivided into 16 fine clusters. The final tissue classifications were presented as color-coded anatomically-mapped images and as two and three dimensional displays of cluster center data in selected feature space (minimum spanning tree). Fuzzy cluster analysis appears to be a clinically useful dimension reduction technique which results in improved diagnostic specificity of medical magnetic resonance images

Automatic detection of multiple UXO-like targets using magnetic anomaly inversion and self-adaptive fuzzy c-means clustering

Science.gov (United States)

Yin, Gang; Zhang, Yingtang; Fan, Hongbo; Ren, Guoquan; Li, Zhining

2017-12-01

We have developed a method for automatically detecting UXO-like targets based on magnetic anomaly inversion and self-adaptive fuzzy c-means clustering. Magnetic anomaly inversion methods are used to estimate the initial locations of multiple UXO-like sources. Although these initial locations have some errors with respect to the real positions, they form dense clouds around the actual positions of the magnetic sources. Then we use the self-adaptive fuzzy c-means clustering algorithm to cluster these initial locations. The estimated number of cluster centroids represents the number of targets and the cluster centroids are regarded as the locations of magnetic targets. Effectiveness of the method has been demonstrated using synthetic datasets. Computational results show that the proposed method can be applied to the case of several UXO-like targets that are randomly scattered within in a confined, shallow subsurface, volume. A field test was carried out to test the validity of the proposed method and the experimental results show that the prearranged magnets can be detected unambiguously and located precisely.

Determination System Of Food Vouchers For the Poor Based On Fuzzy C-Means Method

Science.gov (United States)

Anamisa, D. R.; Yusuf, M.; Syakur, M. A.

2018-01-01

Food vouchers are government programs to tackle the poverty of rural communities. This program aims to help the poor group in getting enough food and nutrients from carbohydrates. There are several factors that influence to receive the food voucher, such as: job, monthly income, Taxes, electricity bill, size of house, number of family member, education certificate and amount of rice consumption every week. In the execution for the distribution of vouchers is often a lot of problems, such as: the distribution of food vouchers has been misdirected and someone who receives is still subjective. Some of the solutions to decision making have not been done. The research aims to calculating the change of each partition matrix and each cluster using Fuzzy C-Means method. Hopefully this research makes contribution by providing higher result using Fuzzy C-Means comparing to other method for this case study. In this research, decision making is done by using Fuzzy C-Means method. The Fuzzy C-Means method is a clustering method that has an organized and scattered cluster structure with regular patterns on two-dimensional datasets. Furthermore, Fuzzy C-Means method used for calculates the change of each partition matrix. Each cluster will be sorted by the proximity of the data element to the centroid of the cluster to get the ranking. Various trials were conducted for grouping and ranking of proposed data that received food vouchers based on the quota of each village. This testing by Fuzzy C-Means method, is developed and abled for determining the recipient of the food voucher with satisfaction results. Fulfillment of the recipient of the food voucher is 80% to 90% and this testing using data of 115 Family Card from 6 Villages. The quality of success affected, has been using the number of iteration factors is 20 and the number of clusters is 3

Soil data clustering by using K-means and fuzzy K-means algorithm

Directory of Open Access Journals (Sweden)

E. Hot

2016-06-01

Full Text Available A problem of soil clustering based on the chemical characteristics of soil, and proper visual representation of the obtained results, is analysed in the paper. To that aim, K-means and fuzzy K-means algorithms are adapted for soil data clustering. A database of soil characteristics sampled in Montenegro is used for a comparative analysis of implemented algorithms. The procedure of setting proper values for control parameters of fuzzy K-means is illustrated on the used database. In addition, validation of clustering is made through visualisation. Classified soil data are presented on the static Google map and dynamic Open Street Map.

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

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Xiangbing Zhou

2018-04-01

Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

Comparing clustering models in bank customers: Based on Fuzzy relational clustering approach

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Ayad Hendalianpour

2016-11-01

Full Text Available Clustering is absolutely useful information to explore data structures and has been employed in many places. It organizes a set of objects into similar groups called clusters, and the objects within one cluster are both highly similar and dissimilar with the objects in other clusters. The K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms are the most popular clustering algorithms for their easy implementation and fast work, but in some cases we cannot use these algorithms. Regarding this, in this paper, a hybrid model for customer clustering is presented that is applicable in five banks of Fars Province, Shiraz, Iran. In this way, the fuzzy relation among customers is defined by using their features described in linguistic and quantitative variables. As follows, the customers of banks are grouped according to K-mean, C-mean, Fuzzy C-mean and Kernel K-mean algorithms and the proposed Fuzzy Relation Clustering (FRC algorithm. The aim of this paper is to show how to choose the best clustering algorithms based on density-based clustering and present a new clustering algorithm for both crisp and fuzzy variables. Finally, we apply the proposed approach to five datasets of customer's segmentation in banks. The result of the FCR shows the accuracy and high performance of FRC compared other clustering methods.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

Science.gov (United States)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

A simple and fast method to determine the parameters for fuzzy c-means cluster analysis

DEFF Research Database (Denmark)

Schwämmle, Veit; Jensen, Ole Nørregaard

2010-01-01

MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values...... of algorithm parameters. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random dataset but which detects cluster...... formation with maximum resolution on the edge of randomness. RESULTS: Estimation of the optimal parameter values is achieved by evaluation of the results of the clustering procedure applied to randomized datasets. In this case, the optimal value of the fuzzifier follows common rules that depend only...

Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods

Science.gov (United States)

Juniati, D.; Khotimah, C.; Wardani, D. E. K.; Budayasa, K.

2018-01-01

The heart abnormalities can be detected from heart sound. A heart sound can be heard directly with a stethoscope or indirectly by a phonocardiograph, a machine of the heart sound recording. This paper presents the implementation of fractal dimension theory to make a classification of phonocardiograms into a normal heart sound, a murmur, or an extrasystole. The main algorithm used to calculate the fractal dimension was Higuchi’s Algorithm. There were two steps to make a classification of phonocardiograms, feature extraction, and classification. For feature extraction, we used Discrete Wavelet Transform to decompose the signal of heart sound into several sub-bands depending on the selected level. After the decomposition process, the signal was processed using Fast Fourier Transform (FFT) to determine the spectral frequency. The fractal dimension of the FFT output was calculated using Higuchi Algorithm. The classification of fractal dimension of all phonocardiograms was done with KNN and Fuzzy c-mean clustering methods. Based on the research results, the best accuracy obtained was 86.17%, the feature extraction by DWT decomposition level 3 with the value of kmax 50, using 5-fold cross validation and the number of neighbors was 5 at K-NN algorithm. Meanwhile, for fuzzy c-mean clustering, the accuracy was 78.56%.

Gas load forecasting based on optimized fuzzy c-mean clustering analysis of selecting similar days

Directory of Open Access Journals (Sweden)

Qiu Jing

2017-08-01

Full Text Available Traditional fuzzy c-means (FCM clustering in short term load forecasting method is easy to fall into local optimum and is sensitive to the initial cluster center.In this paper,we propose to use global search feature of particle swarm optimization (PSO algorithm to avoid these shortcomings,and to use FCM optimization to select similar date of forecast as training sample of support vector machines.This will not only strengthen the data rule of training samples,but also ensure the consistency of data characteristics.Experimental results show that the prediction accuracy of this prediction model is better than that of BP neural network and support vector machine (SVM algorithms.

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

Science.gov (United States)

Akhavan, P.; Karimi, M.; Pahlavani, P.

2014-10-01

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

Directory of Open Access Journals (Sweden)

P. Akhavan

2014-10-01

Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

A physical analogy to fuzzy clustering

DEFF Research Database (Denmark)

Jantzen, Jan

2004-01-01

This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain...

Multivariate spatial condition mapping using subtractive fuzzy cluster means.

Science.gov (United States)

Sabit, Hakilo; Al-Anbuky, Adnan

2014-10-13

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining.

A curvature-based weighted fuzzy c-means algorithm for point clouds de-noising

Science.gov (United States)

Cui, Xin; Li, Shipeng; Yan, Xiutian; He, Xinhua

2018-04-01

In order to remove the noise of three-dimensional scattered point cloud and smooth the data without damnify the sharp geometric feature simultaneity, a novel algorithm is proposed in this paper. The feature-preserving weight is added to fuzzy c-means algorithm which invented a curvature weighted fuzzy c-means clustering algorithm. Firstly, the large-scale outliers are removed by the statistics of r radius neighboring points. Then, the algorithm estimates the curvature of the point cloud data by using conicoid parabolic fitting method and calculates the curvature feature value. Finally, the proposed clustering algorithm is adapted to calculate the weighted cluster centers. The cluster centers are regarded as the new points. The experimental results show that this approach is efficient to different scale and intensities of noise in point cloud with a high precision, and perform a feature-preserving nature at the same time. Also it is robust enough to different noise model.

STUDI SIMULASI MENGGUNAKAN FUZZY C-MEANS DALAM MENGKLASIFIKASI KONSTRUK TES

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Rukli Rukli

2013-01-01

Full Text Available Tulisan ini memperkenalkan metode fuzzy c-means dalam mengklasifikasi konstruk tes. Untuk memverifikasi sifat unidimensional suatu tes biasanya menggunakan analisis faktor sebagai bagian dari statistik parametrik dengan beberapa persyaratan yang ketat sedangkan metode fuzzy c-means termasuk metode heuristik yang tidak memerlukan persyaratan yang ketat. Studi simulasi penelitian ini menggunakan dua metode yakni analisis faktor menggunakan program SPSS dan fuzzy c-means menggunakan program Matlab. Data simulasi menggunakan tipe data dikotomi dan politomi yang dibangkitkan lewat prog-ram Microsoft Office Excel dengan desain 2 kategori, yakni: tiga butir soal dengan banyak peserta tes 10, dan 30 butir soal dengan banyak peserta tes 100. Hasil simulasi menunjukkan bahwa metode fuzzy c-means lebih memberikan gambaran pengelompokan secara deskriptif dan dinamis pada semua desain yang telah dibuat dalam memverifikasi unidimensional pada suatu tes. Kata kunci: fuzzy c-means, analisis faktor, unidimensional _____________________________________________________________ SIMULATION STUDY USING FUZZY C-MEANS FOR CLASIFYING TEST CONSTRUCTION Abstract This paper introduces the fuzzy c-means method for classifying the test constructs. To verify the unidimensional a test typically uses factor analysis as part of parametric statistics with some strict requirements, while fuzzy c-means methods including method heuristic that do not require strict require-ments. Simulation comparison between the method of factor analysis using SPSS program and fuzzy c-means methods using Matlab. Simulation data using data type dichotomy and politomus generated through Microsoft Office Excel programs each with a number of 3 items using the number of participants 10 tests, while the number of 30 test items using the number as many as 100 participants. The simulation results show that the fuzzy c-means method provides a more descriptive and dyna-mic grouping of all the designs that

Distributed k-Means Algorithm and Fuzzy c-Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory.

Science.gov (United States)

Qin, Jiahu; Fu, Weiming; Gao, Huijun; Zheng, Wei Xing

2016-03-03

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

Query by example video based on fuzzy c-means initialized by fixed clustering center

Science.gov (United States)

Hou, Sujuan; Zhou, Shangbo; Siddique, Muhammad Abubakar

2012-04-01

Currently, the high complexity of video contents has posed the following major challenges for fast retrieval: (1) efficient similarity measurements, and (2) efficient indexing on the compact representations. A video-retrieval strategy based on fuzzy c-means (FCM) is presented for querying by example. Initially, the query video is segmented and represented by a set of shots, each shot can be represented by a key frame, and then we used video processing techniques to find visual cues to represent the key frame. Next, because the FCM algorithm is sensitive to the initializations, here we initialized the cluster center by the shots of query video so that users could achieve appropriate convergence. After an FCM cluster was initialized by the query video, each shot of query video was considered a benchmark point in the aforesaid cluster, and each shot in the database possessed a class label. The similarity between the shots in the database with the same class label and benchmark point can be transformed into the distance between them. Finally, the similarity between the query video and the video in database was transformed into the number of similar shots. Our experimental results demonstrated the performance of this proposed approach.

A COMPARISON OF TWO FUZZY CLUSTERING TECHNIQUES

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Samarjit Das

2013-10-01

Full Text Available - In fuzzy clustering, unlike hard clustering, depending on the membership value, a single object may belong exactly to one cluster or partially to more than one cluster. Out of a number of fuzzy clustering techniques Bezdek’s Fuzzy C-Means and GustafsonKessel clustering techniques are well known where Euclidian distance and Mahalanobis distance are used respectively as a measure of similarity. We have applied these two fuzzy clustering techniques on a dataset of individual differences consisting of fifty feature vectors of dimension (feature three. Based on some validity measures we have tried to see the performances of these two clustering techniques from three different aspects- first, by initializing the membership values of the feature vectors considering the values of the three features separately one at a time, secondly, by changing the number of the predefined clusters and thirdly, by changing the size of the dataset.

Brain vascular image segmentation based on fuzzy local information C-means clustering

Science.gov (United States)

Hu, Chaoen; Liu, Xia; Liang, Xiao; Hui, Hui; Yang, Xin; Tian, Jie

2017-02-01

Light sheet fluorescence microscopy (LSFM) is a powerful optical resolution fluorescence microscopy technique which enables to observe the mouse brain vascular network in cellular resolution. However, micro-vessel structures are intensity inhomogeneity in LSFM images, which make an inconvenience for extracting line structures. In this work, we developed a vascular image segmentation method by enhancing vessel details which should be useful for estimating statistics like micro-vessel density. Since the eigenvalues of hessian matrix and its sign describes different geometric structure in images, which enable to construct vascular similarity function and enhance line signals, the main idea of our method is to cluster the pixel values of the enhanced image. Our method contained three steps: 1) calculate the multiscale gradients and the differences between eigenvalues of Hessian matrix. 2) In order to generate the enhanced microvessels structures, a feed forward neural network was trained by 2.26 million pixels for dealing with the correlations between multi-scale gradients and the differences between eigenvalues. 3) The fuzzy local information c-means clustering (FLICM) was used to cluster the pixel values in enhance line signals. To verify the feasibility and effectiveness of this method, mouse brain vascular images have been acquired by a commercial light-sheet microscope in our lab. The experiment of the segmentation method showed that dice similarity coefficient can reach up to 85%. The results illustrated that our approach extracting line structures of blood vessels dramatically improves the vascular image and enable to accurately extract blood vessels in LSFM images.

A fuzzy clustering technique for calorimetric data reconstruction

International Nuclear Information System (INIS)

Sandhir, Radha Pyari; Muhuri, Sanjib; Nayak, Tapan K.

2010-01-01

In high energy physics experiments, electromagnetic calorimeters are used to measure shower particles produced in p-p or heavy-ion collisions. In order to extract information and reconstruct the characteristics of the various incoming particles, clustering is required to be performed on each of the calorimeter planes. Hard clustering techniques such as Local Maxima Search, Connected-cell Search and K-means Clustering simply assign a data point to a cluster. A data point either lies in a cluster or it does not, and so, overlapping clusters are hardly distinguishable. Fuzzy c-means clustering is a version of the k-means algorithm that incorporates fuzzy logic, so that each point has a weak or strong association to the cluster, determined by the inverse distance to the center of the cluster. The term fuzzy is used because an observation may in fact lie in more than one cluster simultaneously, though to different degrees called 'memberships', as is the case with many high energy physics applications. The centers obtained using the FCM algorithm are based on the geometric locations of the data points

Segmentasi Citra USG (Ultrasonography Kanker Payudara Menggunakan Fuzzy C-Means Clustering

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Ri Munarto

2018-01-01

Full Text Available Health is a valuable treasure in survival and can be used as a parameter of quality assurance of human life. Some people even tend to ignore of health, so don’t care about the disease that will them attack and finally to death. Noted the main disease that causes death in the world is cancer. Cancer has many types, but the greatest death in each year is caused by breast cancer. Indonesia found more than 80% of cases in advanced stage, it is estimated that the incidence get 12 people from 10000 women. These numbers will to grow when there is no such treatment as prevention or early diagnosis. Growing of breast cancer patients inversely proportional to the percentage of complaints patients to doctors diagnosis in USG (Ultrasonography breast cancer 20%. The problem is ultrasound imaging which is distorted by speckle noise. The solution is to help easier for doctors to diagnose the presence and form of breast cancer using USG. Speckle noise on USG is able to good reduce using SRAD (Speckle Reducing Anisotropic Diffusion. The filtering results are then well segmented using Fuzzy C-Means Clustering with an accuracy 91.43% of 35 samples USG image breast cancer.

Dynamic Trajectory Extraction from Stereo Vision Using Fuzzy Clustering

Science.gov (United States)

Onishi, Masaki; Yoda, Ikushi

In recent years, many human tracking researches have been proposed in order to analyze human dynamic trajectory. These researches are general technology applicable to various fields, such as customer purchase analysis in a shopping environment and safety control in a (railroad) crossing. In this paper, we present a new approach for tracking human positions by stereo image. We use the framework of two-stepped clustering with k-means method and fuzzy clustering to detect human regions. In the initial clustering, k-means method makes middle clusters from objective features extracted by stereo vision at high speed. In the last clustering, c-means fuzzy method cluster middle clusters based on attributes into human regions. Our proposed method can be correctly clustered by expressing ambiguity using fuzzy clustering, even when many people are close to each other. The validity of our technique was evaluated with the experiment of trajectories extraction of doctors and nurses in an emergency room of a hospital.

A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzy c-Means Clustering

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Li Ma

2015-01-01

Full Text Available Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA. The proposed algorithm combines artificial fish swarm algorithm (AFSA with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM.

Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering

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Ahmed Elazab

2015-01-01

Full Text Available An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.

Fuzzy cluster means algorithm for the diagnosis of confusable disease

African Journals Online (AJOL)

... end platform while Microsoft Access was used as the database application. The system gives a measure of each disease within a set of confusable disease. The proposed system had a classification accuracy of 60%. Keywords: Artificial Intelligence, expert system Fuzzy cluster – means Algorithm, physician, Diagnosis ...

An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm

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Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin

2018-04-01

Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

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Oliynyk Andriy

2012-08-01

Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

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Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano

2012-08-08

Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike

Neuro-fuzzy system modeling based on automatic fuzzy clustering

Institute of Scientific and Technical Information of China (English)

Yuangang TANG; Fuchun SUN; Zengqi SUN

2005-01-01

A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

Application of fuzzy C-Means Algorithm for Determining Field of Interest in Information System Study STTH Medan

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Rahman Syahputra, Edy; Agustina Dalimunthe, Yulia; Irvan

2017-12-01

Many students are confused in choosing their own field of specialization, ultimately choosing areas of specialization that are incompatible with a variety of reasons such as just following a friend or because of the area of interest of many choices without knowing whether they have Competencies in the chosen field of interest. This research aims to apply Clustering method with Fuzzy C-means algorithm to classify students in the chosen interest field. The Fuzzy C-Means algorithm is one of the easiest and often used algorithms in data grouping techniques because it makes efficient estimates and does not require many parameters. Several studies have led to the conclusion that the Fuzzy C-Means algorithm can be used to group data based on certain attributes. In this research will be used Fuzzy C-Means algorithm to classify student data based on the value of core subjects in the selection of specialization field. This study also tested the accuracy of the Fuzzy C-Means algorithm in the determination of interest area. The study was conducted on the STT-Harapan Medan Information System Study program, and the object of research is the value of all students of STT-Harapan Medan Information System Study Program 2012. From this research, it is expected to get the specialization field, according to the students' ability based on the prerequisite principal value.

Optimal Sizing for Wind/PV/Battery System Using Fuzzy c-Means Clustering with Self-Adapted Cluster Number

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Xin Liu

2017-01-01

Full Text Available Integrating wind generation, photovoltaic power, and battery storage to form hybrid power systems has been recognized to be promising in renewable energy development. However, considering the system complexity and uncertainty of renewable energies, such as wind and solar types, it is difficult to obtain practical solutions for these systems. In this paper, optimal sizing for a wind/PV/battery system is realized by trade-offs between technical and economic factors. Firstly, the fuzzy c-means clustering algorithm was modified with self-adapted parameters to extract useful information from historical data. Furthermore, the Markov model is combined to determine the chronological system states of natural resources and load. Finally, a power balance strategy is introduced to guide the optimization process with the genetic algorithm to establish the optimal configuration with minimized cost while guaranteeing reliability and environmental factors. A case of island hybrid power system is analyzed, and the simulation results are compared with the general FCM method and chronological method to validate the effectiveness of the mentioned method.

Improved Fuzzy Art Method for Initializing K-means

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Sevinc Ilhan

2010-09-01

Full Text Available The K-means algorithm is quite sensitive to the cluster centers selected initially and can perform different clusterings depending on these initialization conditions. Within the scope of this study, a new method based on the Fuzzy ART algorithm which is called Improved Fuzzy ART (IFART is used in the determination of initial cluster centers. By using IFART, better quality clusters are achieved than Fuzzy ART do and also IFART is as good as Fuzzy ART about capable of fast clustering and capability on large scaled data clustering. Consequently, it is observed that, with the proposed method, the clustering operation is completed in fewer steps, that it is performed in a more stable manner by fixing the initialization points and that it is completed with a smaller error margin compared with the conventional K-means.

Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier

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Krishna Kant Singh

2017-06-01

Full Text Available A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

Fuzzy Rules for Ant Based Clustering Algorithm

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Amira Hamdi

2016-01-01

Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.

AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION

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V.G. Biju

2015-11-01

Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.

A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

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Osama Moh’d Alia

2014-01-01

Full Text Available Energy conservation in wireless sensor networks (WSNs is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network’s lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols.

A decentralized fuzzy C-means-based energy-efficient routing protocol for wireless sensor networks.

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Alia, Osama Moh'd

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols.

A Decentralized Fuzzy C-Means-Based Energy-Efficient Routing Protocol for Wireless Sensor Networks

Science.gov (United States)

2014-01-01

Energy conservation in wireless sensor networks (WSNs) is a vital consideration when designing wireless networking protocols. In this paper, we propose a Decentralized Fuzzy Clustering Protocol, named DCFP, which minimizes total network energy dissipation to promote maximum network lifetime. The process of constructing the infrastructure for a given WSN is performed only once at the beginning of the protocol at a base station, which remains unchanged throughout the network's lifetime. In this initial construction step, a fuzzy C-means algorithm is adopted to allocate sensor nodes into their most appropriate clusters. Subsequently, the protocol runs its rounds where each round is divided into a CH-Election phase and a Data Transmission phase. In the CH-Election phase, the election of new cluster heads is done locally in each cluster where a new multicriteria objective function is proposed to enhance the quality of elected cluster heads. In the Data Transmission phase, the sensing and data transmission from each sensor node to their respective cluster head is performed and cluster heads in turn aggregate and send the sensed data to the base station. Simulation results demonstrate that the proposed protocol improves network lifetime, data delivery, and energy consumption compared to other well-known energy-efficient protocols. PMID:25162060

Fuzzy C-Means Algorithm for Segmentation of Aerial Photography Data Obtained Using Unmanned Aerial Vehicle

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Akinin, M. V.; Akinina, N. V.; Klochkov, A. Y.; Nikiforov, M. B.; Sokolova, A. V.

2015-05-01

The report reviewed the algorithm fuzzy c-means, performs image segmentation, give an estimate of the quality of his work on the criterion of Xie-Beni, contain the results of experimental studies of the algorithm in the context of solving the problem of drawing up detailed two-dimensional maps with the use of unmanned aerial vehicles. According to the results of the experiment concluded that the possibility of applying the algorithm in problems of decoding images obtained as a result of aerial photography. The considered algorithm can significantly break the original image into a plurality of segments (clusters) in a relatively short period of time, which is achieved by modification of the original k-means algorithm to work in a fuzzy task.

Extended Traffic Crash Modelling through Precision and Response Time Using Fuzzy Clustering Algorithms Compared with Multi-layer Perceptron

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Iman Aghayan

2012-11-01

Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.

A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine

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Zhi-tao Wang

2015-01-01

Full Text Available As an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM clustering algorithm and support vector machine (SVM classification model is proposed in this paper. Considering the distribution characteristics of gas turbine EGT, FCM clustering algorithm is used to realize clustering analysis and obtain the state pattern, on the basis of which the preclassification of EGT is completed. Then, SVM multiclassification model is designed to carry out the state pattern recognition and fault diagnosis. As an example, the historical monitoring data of EGT from an industrial gas turbine is analyzed and used to verify the performance of the fusion fault diagnosis approach presented in this paper. The results show that this approach can make full use of the unsupervised feature extraction ability of FCM clustering algorithm and the sample classification generalization properties of SVM multiclassification model, which offers an effective way to realize the online condition recognition and fault diagnosis of gas turbine EGT.

Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input function.

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Yin, Jiandong; Sun, Hongzan; Yang, Jiawen; Guo, Qiyong

2014-01-01

The arterial input function (AIF) plays a crucial role in the quantification of cerebral perfusion parameters. The traditional method for AIF detection is based on manual operation, which is time-consuming and subjective. Two automatic methods have been reported that are based on two frequently used clustering algorithms: fuzzy c-means (FCM) and K-means. However, it is still not clear which is better for AIF detection. Hence, we compared the performance of these two clustering methods using both simulated and clinical data. The results demonstrate that K-means analysis can yield more accurate and robust AIF results, although it takes longer to execute than the FCM method. We consider that this longer execution time is trivial relative to the total time required for image manipulation in a PACS setting, and is acceptable if an ideal AIF is obtained. Therefore, the K-means method is preferable to FCM in AIF detection.

Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI

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Nour-Eddine El Harchaoui

2013-01-01

Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.

Fuzzy C-means classification for corrosion evolution of steel images

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Trujillo, Maite; Sadki, Mustapha

2004-05-01

An unavoidable problem of metal structures is their exposure to rust degradation during their operational life. Thus, the surfaces need to be assessed in order to avoid potential catastrophes. There is considerable interest in the use of patch repair strategies which minimize the project costs. However, to operate such strategies with confidence in the long useful life of the repair, it is essential that the condition of the existing coatings and the steel substrate can be accurately quantified and classified. This paper describes the application of fuzzy set theory for steel surfaces classification according to the steel rust time. We propose a semi-automatic technique to obtain image clustering using the Fuzzy C-means (FCM) algorithm and we analyze two kinds of data to study the classification performance. Firstly, we investigate the use of raw images" pixels without any pre-processing methods and neighborhood pixels. Secondly, we apply Gaussian noise to the images with different standard deviation to study the FCM method tolerance to Gaussian noise. The noisy images simulate the possible perturbations of the images due to the weather or rust deposits in the steel surfaces during typical on-site acquisition procedures

Model of cholera dissemination using geographic information systems and fuzzy clustering means: case study, Chabahar, Iran.

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Pezeshki, Z; Tafazzoli-Shadpour, M; Mansourian, A; Eshrati, B; Omidi, E; Nejadqoli, I

2012-10-01

Cholera is spread by drinking water or eating food that is contaminated by bacteria, and is related to climate changes. Several epidemics have occurred in Iran, the most recent of which was in 2005 with 1133 cases and 12 deaths. This study investigated the incidence of cholera over a 10-year period in Chabahar district, a region with one of the highest incidence rates of cholera in Iran. Descriptive retrospective study on data of patients with Eltor and NAG cholera reported to the Iranian Centre of Disease Control between 1997 and 2006. Data on the prevalence of cholera were gathered through a surveillance system, and a spatial database was developed using geographic information systems (GIS) to describe the relation of spatial and climate variables to cholera incidences. Fuzzy clustering (fuzzy C) method and statistical analysis based on logistic regression were used to develop a model of cholera dissemination. The variables were demographic characteristics, specifications of cholera infection, climate conditions and some geographical parameters. The incidence of cholera was found to be significantly related to higher temperature and humidity, lower precipitation, shorter distance to the eastern border of Iran and local health centres, and longer distance to the district health centre. The fuzzy C means algorithm showed that clusters were geographically distributed in distinct regions. In order to plan, manage and monitor any public health programme, GIS provide ideal platforms for the convergence of disease-specific information, analysis and computation of new data for statistical analysis. Copyright © 2012 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

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Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun

2017-01-01

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.

SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

International Nuclear Information System (INIS)

Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu; Pradier, Olivier; Cheze Le Rest, Catherine

2015-01-01

Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

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Ioannis Panapakidis

2017-09-01

Full Text Available Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets.

Comparative Investigation of Guided Fuzzy Clustering and Mean Shift Clustering for Edge Detection in Electrical Resistivity Tomography Images of Mineral Deposits

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Ward, Wil; Wilkinson, Paul; Chambers, Jon; Bai, Li

2014-05-01

is an optimised choice of kernel size, for which automated procedures are available. In general, since the gfcm does not require a convergence criterion it is notably faster than mean-shift. The mean-shift approach is sensitive to small changes of the bandwidth, whereas with gfcm the guiding pdf approximation can be intermediately reviewed and smoothed if desired. In mapping the resistivity information to 1D in pre-processing, no assumptions on the spatial shape of the clusters are needed, a common problem in centroid-based clustering. References: [1] River Terrace Sand and Gravel Deposit Reserve Estimation Using Three Dimensional Electrical Resistivity Tomography for Bedrock Surface Detection. Journal of Applied Geophysics, 2013. Chambers, J.E. et al. [2] Distribution-based Fuzzy Clustering of Electrical Resistivity Tomography Images for Interface Detection. Geophysical Journal International, 2014. Ward W.O.C. et al. [3] Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002. Comaniciu, D and Meer, P.

Improved R2* liver iron concentration assessment using a novel fuzzy c-mean clustering scheme

International Nuclear Information System (INIS)

Saiviroonporn, Pairash; Viprakasit, Vip; Krittayaphong, Rungroj

2015-01-01

In thalassemia patients, R2* liver iron concentration (LIC) measurement is a common clinical tool for assessing iron overload and for determining necessary chelator dose and evaluating its efficacy. Despite the importance of accurate LIC measurement, existing methods suffer from LIC variability, especially at the severe iron overload range due to inclusion of vessel parts in LIC calculation. In this study, we build upon previous Fuzzy C-Mean (FCM) clustering work to formulate a scheme with superior performance in segmenting vessel pixels from the parenchyma. Our method (MIX-FCM) combines our novel 2D-FCM with the existing 1D-FCM algorithm. This study further assessed possible optimal clustering parameters (OP scheme) and proposed a semi-automatic (SA) scheme for routine clinical application. Segmentation of liver parenchyma and vessels was performed on T2* images and their LIC maps in 196 studies from 147 thalassemia major patients. We used manual segmentation as the reference. 1D-FCM clustering was performed on the acquired image alone and 2D-FCM used both the acquired image and its LIC data. To execute the MIX-FCM method, the best outcome (OP-MIX-FCM) was selected from the aforementioned methods and was compared to the SA-MIX-FCM scheme. We used the percent value of the normalized interquartile range (nIQR) to its median to evaluate the variability of all methods. 2D-FCM clustering is more effective than 1D-FCM clustering at the severe overload range only, but inferior for other ranges (where 1D-FCM provides suitable results). This complementary performance between the two methods allows MIX-FCM to improve results for all ranges. OP-MIX-FCM clustering error was 2.1 ± 2.3 %, compared with 10.3 ± 9.9 % and 7.0 ± 11.9 % from 1D- and 2D-FCM clustering, respectively. SA-MIX-FCM result was comparable to OP-MIX-FCM result, with both schemes showing ability to decrease overall nIQR by approximately 30 %. Our proposed 2D-FCM algorithm is not as superior to 1D-FCM as

Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support

OpenAIRE

S. Parkash  Kumar; K. S. Ramaswami

2011-01-01

Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using...

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

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Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

2015-07-01

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

Science.gov (United States)

Chahine, Firas Safwan

2012-01-01

Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm

Science.gov (United States)

Ayvaz, M. Tamer

2007-11-01

This study proposes an inverse solution algorithm through which both the aquifer parameters and the zone structure of these parameters can be determined based on a given set of observations on piezometric heads. In the zone structure identification problem fuzzy c-means ( FCM) clustering method is used. The association of the zone structure with the transmissivity distribution is accomplished through an optimization model. The meta-heuristic harmony search ( HS) algorithm, which is conceptualized using the musical process of searching for a perfect state of harmony, is used as an optimization technique. The optimum parameter zone structure is identified based on three criteria which are the residual error, parameter uncertainty, and structure discrimination. A numerical example given in the literature is solved to demonstrate the performance of the proposed algorithm. Also, a sensitivity analysis is performed to test the performance of the HS algorithm for different sets of solution parameters. Results indicate that the proposed solution algorithm is an effective way in the simultaneous identification of aquifer parameters and their corresponding zone structures.

Eigenspace-based fuzzy c-means for sensing trending topics in Twitter

Science.gov (United States)

Muliawati, T.; Murfi, H.

2017-07-01

As the information and communication technology are developed, the fulfillment of information can be obtained through social media, like Twitter. The enormous number of internet users has triggered fast and large data flow, thus making the manual analysis is difficult or even impossible. An automated methods for data analysis is needed, one of which is the topic detection and tracking. An alternative method other than latent Dirichlet allocation (LDA) is a soft clustering approach using Fuzzy C-Means (FCM). FCM meets the assumption that a document may consist of several topics. However, FCM works well in low-dimensional data but fails in high-dimensional data. Therefore, we propose an approach where FCM works on low-dimensional data by reducing the data using singular value decomposition (SVD). Our simulations show that this approach gives better accuracies in term of topic recall than LDA for sensing trending topic in Twitter about an event.

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

Energy Technology Data Exchange (ETDEWEB)

Tsantis, Stavros [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Spiliopoulos, Stavros; Karnabatidis, Dimitrios [Department of Radiology, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Skouroliakou, Aikaterini [Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210 (Greece); Hazle, John D. [Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Kagadis, George C., E-mail: gkagad@gmail.com, E-mail: George.Kagadis@med.upatras.gr, E-mail: GKagadis@mdanderson.org [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States)

2014-07-15

Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A

Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

International Nuclear Information System (INIS)

Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitrios; Skouroliakou, Aikaterini; Hazle, John D.; Kagadis, George C.

2014-01-01

Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A

KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance

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Zhiwen ZU

2018-04-01

Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.

Sistem Perencanaan Penambahan Stok Barang menggunakan Metode Fuzzy C-Means dan Fuzzy Tsukamoto (Studi Kasus di Distributor Alfamart Semarang

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Tono Puryanto

2016-08-01

Full Text Available Gudang barang suatu perusahaan merupakan tempat penyimpanan barang yang akan dijual kepada pelanggan. Permasalahan utama pada gudang barang suatu perusahaan adalah terjadinya penumpukan barang atau barang keluar lebih banyak daripada barang masuk yang dapat mengakibatkan kerugian bagi perusahaan. Penambahan stok barang pada gudang dilakukan berdasarkan permintaan pelanggan dan stok barang saat itu. Banyak permintaan pelanggan setiap waktu selalu berubah yang dapat menyebabkan terjadinya penumpukan barang atau kekurangan barang. Hal ini menyebabkan sulit dalam pengambilan keputusan jumlah barang yang akan dikirim. Salah satu cara untuk membantu pengambilan keputusan tersebut yaitu dengan pembangunan aplikasi perencanaan penambahan stok barang yang menggunakan konsep logika fuzzy. Fuzzy merupakan suatu cara untuk menyelesaikan masalah ketidakpastian. Pada aplikasi perencanaan penambahan stok barang, proses penentuan penambahan stok barang dilakukan dengan menggunakan metode fuzzy C-Means dan mekanisme inferensi fuzzy Tsukamoto. Hasil akhir dari aplikasi ini berupa jumlah barang yang akan dikirim. Hasil tersebut menjadi saran yang dapat dipertimbangkan oleh admin bagian pengiriman barang. Pengujian dilakukan menggunakan data Coca-Cola pada bulan September 2014 sampai Oktober 2014. Pada pengujian sistem dilakukan 11 kali pengujian dengan memasukkan stok dan permintaan data asli menghasilkan tingkat keakuratan sistem sebesar 80,22 %. Tingkat keakuratan sistem dapat berubah tergantung pada data pelatihan yang digunakan pada proses pelatihan fuzzy C-Means.

Normed kernel function-based fuzzy possibilistic C-means (NKFPCM) algorithm for high-dimensional breast cancer database classification with feature selection is based on Laplacian Score

Science.gov (United States)

Lestari, A. W.; Rustam, Z.

2017-07-01

In the last decade, breast cancer has become the focus of world attention as this disease is one of the primary leading cause of death for women. Therefore, it is necessary to have the correct precautions and treatment. In previous studies, Fuzzy Kennel K-Medoid algorithm has been used for multi-class data. This paper proposes an algorithm to classify the high dimensional data of breast cancer using Fuzzy Possibilistic C-means (FPCM) and a new method based on clustering analysis using Normed Kernel Function-Based Fuzzy Possibilistic C-Means (NKFPCM). The objective of this paper is to obtain the best accuracy in classification of breast cancer data. In order to improve the accuracy of the two methods, the features candidates are evaluated using feature selection, where Laplacian Score is used. The results show the comparison accuracy and running time of FPCM and NKFPCM with and without feature selection.

New Embedded Denotes Fuzzy C-Mean Application for Breast Cancer Density Segmentation in Digital Mammograms

Science.gov (United States)

Othman, Khairulnizam; Ahmad, Afandi

2016-11-01

In this research we explore the application of normalize denoted new techniques in advance fast c-mean in to the problem of finding the segment of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if new denotes fuzzy c- mean algorithm could separate different densities for the different breast patterns. The new density segmentation is applied with multi-selection of seeds label to provide the hard constraint, whereas the seeds labels are selected based on user defined. New denotes fuzzy c- mean have been explored on images of various imaging modalities but not on huge format digital mammograms just yet. Therefore, this project is mainly focused on using normalize denoted new techniques employed in fuzzy c-mean to perform segmentation to increase visibility of different breast densities in mammography images. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology for the segmentation of mammograms on the basis of their region into different densities based categories has been tested on MIAS database and Trueta Database.

Comments on "The multisynapse neural network and its application to fuzzy clustering".

Science.gov (United States)

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

Model of spatial analysis of electric power market using the Fuzzy C-Means technical classification; Modelo de analise espacial de mercado de energia eletrica utilizando a tecnica de classificacao Fuzzy C-Means

Energy Technology Data Exchange (ETDEWEB)

Neto, J.C. [Companhia Energetica de Goias (CELG-D), Goiania, GO (Brazil)], E-mail: joao.cn@celg.com.br; Lima, W.S. [Votorantim Siderurgia, Resende, Rio de Janeiro, RJ (Brazil). Gerencia Geral de Tecnologia], E-mail: wagner.lima@vmetais.com.br

2009-07-01

The power distribution companies live with an antagonistic reality: an increasing energy demand, due to steady economic and population growth, and a limitation on their financial resources to expand its network. Therefore, it is essential an improvement in activity of planning of the power distribution system trying to improve the application of available resources. In this context fits the application of Geographic Information System combined with clustering techniques and classification in order to enhance the planning process, giving the planner a more complete picture of the consumer market by the distributor. This paper presents a system that makes use of Geographic Information System combined with the technique of clustering and classification Fuzzy C-Means, with the aim of analyzing the distribution of network load and the performance of the technique. Each group performed leads to a spatial representation (scenario). This, together with an index measuring the performance of the group (intra-group and inter-group) implemented in this work, provides a favorable environment for spatial analysis of the electric power market.

Penerapan Fuzzy C-Means Untuk Penentuan Besar Uang Kuliah Tunggal Mahasiswa Baru

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Ariyady Kurniawan Muchsin

2015-12-01

Full Text Available In accordance with the mandate of the 1945 Constitution article 31 concerning the education authorities have issued various policies to realize the cost of education is getting cheaper and affordable to all people, one of which is the system UKT (Tuition Single which is partially Tuition Single (BKT which were passed to each student based on their economic capabilities. UKT grouping mechanism is still done manually by Udayana University which resulted in the value of equity for prospective new students to their economic capacity is still lacking. Therefore, it needs a mechanism for charging and determination UKT which can be done online, so as to improve efficiency and effectiveness. The next solution that can be done is by using classification techniques using Fuzzy C-Means (FCM and Beni Xie Index to determine the optimum clusters in the process of determining the type UKT so as to meet the values ??of justice for prospective new students.

An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

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Elid Rubio

2017-01-01

Full Text Available In this work an extension of the Fuzzy Possibilistic C-Means (FPCM algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM algorithm to observe if the proposed approach has better performance than this algorithm.

Intuitionistic fuzzy aggregation and clustering

CERN Document Server

Xu, Zeshui

2012-01-01

This book offers a systematic introduction to the clustering algorithms for intuitionistic fuzzy values, the latest research results in intuitionistic fuzzy aggregation techniques, the extended results in interval-valued intuitionistic fuzzy environments, and their applications in multi-attribute decision making, such as supply chain management, military system performance evaluation, project management, venture capital, information system selection, building materials classification, and operational plan assessment, etc.

A fuzzy Hopfield neural network for medical image segmentation

International Nuclear Information System (INIS)

Lin, J.S.; Cheng, K.S.; Mao, C.W.

1996-01-01

In this paper, an unsupervised parallel segmentation approach using a fuzzy Hopfield neural network (FHNN) is proposed. The main purpose is to embed fuzzy clustering into neural networks so that on-line learning and parallel implementation for medical image segmentation are feasible. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to class centers. In order to generate feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function, which is formulated and based on a basic concept commonly used in pattern classification, called the within-class scatter matrix principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The fuzzy Hopfield neural network based on the within-class scatter matrix shows the promising results in comparison with the hard c-means method

Distribution-based fuzzy clustering of electrical resistivity tomography images for interface detection

Science.gov (United States)

Ward, W. O. C.; Wilkinson, P. B.; Chambers, J. E.; Oxby, L. S.; Bai, L.

2014-04-01

A novel method for the effective identification of bedrock subsurface elevation from electrical resistivity tomography images is described. Identifying subsurface boundaries in the topographic data can be difficult due to smoothness constraints used in inversion, so a statistical population-based approach is used that extends previous work in calculating isoresistivity surfaces. The analysis framework involves a procedure for guiding a clustering approach based on the fuzzy c-means algorithm. An approximation of resistivity distributions, found using kernel density estimation, was utilized as a means of guiding the cluster centroids used to classify data. A fuzzy method was chosen over hard clustering due to uncertainty in hard edges in the topography data, and a measure of clustering uncertainty was identified based on the reciprocal of cluster membership. The algorithm was validated using a direct comparison of known observed bedrock depths at two 3-D survey sites, using real-time GPS information of exposed bedrock by quarrying on one site, and borehole logs at the other. Results show similarly accurate detection as a leading isosurface estimation method, and the proposed algorithm requires significantly less user input and prior site knowledge. Furthermore, the method is effectively dimension-independent and will scale to data of increased spatial dimensions without a significant effect on the runtime. A discussion on the results by automated versus supervised analysis is also presented.

Fuzzy sets, rough sets, multisets and clustering

CERN Document Server

Dahlbom, Anders; Narukawa, Yasuo

2017-01-01

This book is dedicated to Prof. Sadaaki Miyamoto and presents cutting-edge papers in some of the areas in which he contributed. Bringing together contributions by leading researchers in the field, it concretely addresses clustering, multisets, rough sets and fuzzy sets, as well as their applications in areas such as decision-making. The book is divided in four parts, the first of which focuses on clustering and classification. The second part puts the spotlight on multisets, bags, fuzzy bags and other fuzzy extensions, while the third deals with rough sets. Rounding out the coverage, the last part explores fuzzy sets and decision-making.

Information Clustering Based on Fuzzy Multisets.

Science.gov (United States)

Miyamoto, Sadaaki

2003-01-01

Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

Science.gov (United States)

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Science.gov (United States)

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering

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Maulana Erwin Saputra

2017-06-01

Full Text Available In the first journal, I made this attempt to analyze things that affect the achievement of students in each school of course vary. Because students are one of the goals of achieving the goals of successful educational organizations. The mental influence of students’ emotions and behaviors themselves in relation to learning performance. Fuzzy logic can be used in various fields as well as Clustering for grouping, as in Learning Development analyzes. The process will be performed on students based on the symptoms that exist. In this research will use fuzzy logic and clustering. Fuzzy is an uncertain logic but its excess is capable in the process of language reasoning so that in its design is not required complicated mathematical equations. However Clustering method is K-Means method is method where data analysis is broken down by group k (k = 1,2,3, .. k. To know the optimal number of Performance group. The results of the research is with a questionnaire entered into matlab will produce a value that means in generating the graph. And simplify the school in seeing Student performance in the learning process by using certain criteria. So from the system that obtained the results for a decision-making required by the school.

Fuzzy Clustering Methods and their Application to Fuzzy Modeling

DEFF Research Database (Denmark)

Kroszynski, Uri; Zhou, Jianjun

1999-01-01

Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate....... An illustrative synthetic example is analyzed, and prediction accuracy measures are compared between the different variants...

Fuzzy clustering-based segmented attenuation correction in whole-body PET

CERN Document Server

Zaidi, H; Boudraa, A; Slosman, DO

2001-01-01

Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...

Application of Bibliographic Coupling versus Cited Titles Words in Patent Fuzzy Clustering

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Anahita Kermani

2013-03-01

Full Text Available Attribute selection is one of the steps before patent clustering. Various attributes can be used for clustering. In this study, the effect of using citation and citation title words, respectively, in form of bibliographic coupling and citation title words sharing, were measured and compared with each other, as patent attributes. This study was done in an experimental method, on a collection of 717 US Patent cited in the patents belong to 977/774 subclass of US Patent Classification. Fuzzy C-means was used for patent clustering and extended BCubed precision and extended BCubed recall were used as evaluation measure. The results showed that the clustering produced by bibliographic coupling had better performance than clustering used citation title words and existence of cluster structure were in a wider range of exhaustivity than citation title words.

Cluster analysis by optimal decomposition of induced fuzzy sets

Energy Technology Data Exchange (ETDEWEB)

Backer, E

1978-01-01

Nonsupervised pattern recognition is addressed and the concept of fuzzy sets is explored in order to provide the investigator (data analyst) additional information supplied by the pattern class membership values apart from the classical pattern class assignments. The basic ideas behind the pattern recognition problem, the clustering problem, and the concept of fuzzy sets in cluster analysis are discussed, and a brief review of the literature of the fuzzy cluster analysis is given. Some mathematical aspects of fuzzy set theory are briefly discussed; in particular, a measure of fuzziness is suggested. The optimization-clustering problem is characterized. Then the fundamental idea behind affinity decomposition is considered. Next, further analysis takes place with respect to the partitioning-characterization functions. The iterative optimization procedure is then addressed. The reclassification function is investigated and convergence properties are examined. Finally, several experiments in support of the method suggested are described. Four object data sets serve as appropriate test cases. 120 references, 70 figures, 11 tables. (RWR)

Segmentation of dermatoscopic images by frequency domain filtering and k-means clustering algorithms.

Science.gov (United States)

Rajab, Maher I

2011-11-01

Since the introduction of epiluminescence microscopy (ELM), image analysis tools have been extended to the field of dermatology, in an attempt to algorithmically reproduce clinical evaluation. Accurate image segmentation of skin lesions is one of the key steps for useful, early and non-invasive diagnosis of coetaneous melanomas. This paper proposes two image segmentation algorithms based on frequency domain processing and k-means clustering/fuzzy k-means clustering. The two methods are capable of segmenting and extracting the true border that reveals the global structure irregularity (indentations and protrusions), which may suggest excessive cell growth or regression of a melanoma. As a pre-processing step, Fourier low-pass filtering is applied to reduce the surrounding noise in a skin lesion image. A quantitative comparison of the techniques is enabled by the use of synthetic skin lesion images that model lesions covered with hair to which Gaussian noise is added. The proposed techniques are also compared with an established optimal-based thresholding skin-segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties, the k-means clustering and fuzzy k-means clustering segmentation methods provide the best performance over a range of signal to noise ratios. The proposed segmentation techniques are also demonstrated to have similar performance when tested on real skin lesions representing high-resolution ELM images. This study suggests that the segmentation results obtained using a combination of low-pass frequency filtering and k-means or fuzzy k-means clustering are superior to the result that would be obtained by using k-means or fuzzy k-means clustering segmentation methods alone. © 2011 John Wiley & Sons A/S.

Classifying OECD Countries According to Health Indicators Using Fuzzy Clustering Ana lysis

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Nesrin Alptekin

2015-12-01

Full Text Available This study was conducted in order to classify OECD countries according to health indicators using fuzzy clustering analysis, to identify the cluster in which Turkey is in and the other countries located in the same cluster with Turkey and to determine whether Turkey shows similar characteristics with other countries located in the same cluster or not. In the study, 34 OECD member countries were discussed. With ten variables that directly and indirectly affect the health, c- means clustering analysis was performed. The NCSS 10 software package was used to analyze the data.In the analysis, it was determined that the most appropriate cluster number is five; three countries involved in the first cluster, nine countries involved in the second cluster, nine countries involved in the third cluster, six countries involved in the fourth cluster and seven countries involved in the fifth cluster. Turkey is located in the fourth cluster. Other countries in the same cluster along with Turkey are Estonia, Hungary, Mexico, Poland and Chile

A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks

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ZiQi Hao

2015-01-01

Full Text Available As limited energy is one of the tough challenges in wireless sensor networks (WSN, energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We use k-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.

An Application of Fuzzy Inference System by Clustering Subtractive Fuzzy Method for Estimating of Product Requirement

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Fajar Ibnu Tufeil

2009-06-01

Full Text Available Model fuzzy memiliki kemampuan untuk menjelaskan secara linguistik suatu sistem yang terlalu kompleks. Aturan-aturan dalam model fuzzy pada umumnya dibangun berdasarkan keahlian manusia dan pengetahuan heuristik dari sistem yang dimodelkan. Teknik ini selanjutnya dikembangkan menjadi teknik yang dapat mengidentifikasi aturan-aturan dari suatu basis data yang telah dikelompokkan berdasarkan persamaan strukturnya. Dalam hal ini metode pengelompokan fuzzy berfungsi untuk mencari kelompok-kelompok data. Informasi yang dihasilkan dari metode pengelompokan ini, yaitu informasi tentang pusat kelompok, digunakan untuk membentuk aturan-aturan dalam sistem penalaran fuzzy. Dalam skripsi ini dibahas mengenai penerapan fuzzy infereance system dengan metode pengelompokan fuzzy subtractive clustering, yaitu untuk membentuk sistem penalaran fuzzy dengan menggunakan model fuzzy Takagi-Sugeno orde satu. Selanjutnya, metode pengelompokan fuzzy subtractive clustering diterapkan dalam memodelkan masalah dibidang pemasaran, yaitu untuk memprediksi permintaan pasar terhadap suatu produk susu. Aplikasi ini dibangun menggunakan Borland Delphi 6.0. Dari hasil pengujian diperoleh tingkat error prediksi terkecil yaitu dengan Error Average 0.08%.

Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions.

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Abe, S

1998-01-01

In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables.

Pattern Classification of Tropical Cyclone Tracks over the Western North Pacific using a Fuzzy Clustering Method

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Kim, H.; Ho, C.; Kim, J.

2008-12-01

This study presents the pattern classification of tropical cyclone (TC) tracks over the western North Pacific (WNP) basin during the typhoon season (June through October) for 1965-2006 (total 42 years) using a fuzzy clustering method. After the fuzzy c-mean clustering algorithm to the TC trajectory interpolated into 20 segments of equivalent length, we divided the whole tracks into 7 patterns. The optimal number of the fuzzy cluster is determined by several validity measures. The classified TC track patterns represent quite different features in the recurving latitudes, genesis locations, and geographical pathways: TCs mainly forming in east-northern part of the WNP and striking Korean and Japan (C1); mainly forming in west-southern part of the WNP, traveling long pathway, and partly striking Japan (C2); mainly striking Taiwan and East China (C3); traveling near the east coast of Japan (C4); traveling the distant ocean east of Japan (C5); moving toward South China and Vietnam straightly (C6); and forming in the South China Sea (C7). Atmospheric environments related to each cluster show physically consistent with each TC track patterns. The straight track pattern is closely linked to a developed anticyclonic circulation to the north of the TC. It implies that this ridge acts as a steering flow forcing TCs to move to the northwest with a more west-oriented track. By contrast, recurving patterns occur commonly under the influence of the strong anomalous westerlies over the TC pathway but there definitely exist characteristic anomalous circulations over the mid- latitudes by pattern. Some clusters are closely related to the well-known large-scale phenomena. The C1 and C2 are highly related to the ENSO phase: The TCs in the C1 (C2) is more active during La Niña (El Niño). The TC activity in the C3 is associated with the WNP summer monsoon. The TCs in the C4 is more (less) vigorous during the easterly (westerly) phase of the stratospheric quasi-biennial oscillation

An Interval Type-2 Fuzzy C-Means algorithm for image segmentation%区间二型模糊C均值聚类在图像分割中的应用

Institute of Scientific and Technical Information of China (English)

邱存勇; 肖建

2011-01-01

Cluster analysis is an important branch of non-supervision pattern recognition, and Fuzzy C-Means(FCM) algorithm is a classic algorithm in cluster analysis. However, FCM is founded with Type-1 fuzzy sets, which can not handle the uncertainties existing in data and algorithm itself. This paper introduces the Interval Type-2 Fuzzy C-Means(IT2FCM) algorithm, whose core is type-2 fuzzy set that has better performance on handling uncertainties than Type-1 fuzzy set. IT2FCM and FCM are used for image segmentation to compare their segmentation results. The experiment shows that IT2FCM has better performance on suppressing noise and better effects on segmenting images compared with FCM.%聚类分析是非监督模式识别的重要分支,模糊C均值聚类算法(FCM)是其中的一类经典算法,然而该算法以一型模糊集为基础,无法处理数据集以及算法中的不确定性,为此引入区间二型模糊C均值聚类算法(IT2FCM).二型模糊集处理不确定性的能力强于一型模糊集,基于二型模糊集的IT2FCM在处理不确定性时效果优于FCM算法.文章以图像分割为应用对象,比较IT2FCM和FCM算法的分割效果,实验证明IT2FCM较传统FCM有更好的抗噪性.

Optimasi Penempatan Lokasi Potensial Menara Baru Bersama Pada Sistem Telekomunikasi Seluler Dengan Menggunakan Fuzzy Clustering Di Daerah Sidoarjo

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Muthmainnah Muthmainnah

2015-03-01

Full Text Available Mengikuti perkembangan jumlah pelanggan seluler yang semakin pesat, para operator terus berusaha membangun infrastruktur agar layanan dan kualitasnya semakin meningkat. Salah satu infrastruktur penyelenggaraan yang terus menerus dibangun adalah Base Transceiver Station. Namun, pembangunan BTS tersebut harus mempertimbangkan estetika dan kesesuaian dengan Rencana Tata Ruang Wilayah (RTRW. Tugas akhir ini bertujuan untuk menerapkan metode Fuzzy Clustering dan Harmony Search untuk mengoptimalkan penempatan lokasi potensial menara baru sehingga diperoleh solusi yang optimal. Selain RTRW, titik potensial juga dapat ditentukan dengan menggunakan titik pusat cluster melalui metode Fuzzy C-Means. Setelah itu titik menara baru dapat dioptimasi dengan menggunakan metode Harmony Search dengan meminimalkan fungsi path loss. Hasil optimasi menunjukan bahwa untuk layanan 2G membutuhkan penambahan BTS sebanyak 343 BTS yang mampu melayani kebutuhan trafik sebesar 42230 Erlang, sedangkan untuk layanan 3G membutuhkan penambahan BTS sebanyak 278 BTS yang mampu melayani Offered Bit Quantity (OBQ sebesar 1160857  Kbps dengan total luas coverage BTS nya sebesar 60.798 km2. namun dari segi jumlah menaranya tidak terjadi penambahan pada kedua jenis layanan ini. Hal ini dimaksudkan agar dapat mengefisienkan penggunaan menara eksisting. Dengan menggunakan metode Fuzzy Subtractive Clustering diperoleh 3 (tiga jumlah cluster yang optimal di setiap kecamatan.

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach.

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Julie, E Golden; Selvi, S Tamil

2016-01-01

Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

a novel two – factor high order fuzzy time series with applications to ...

African Journals Online (AJOL)

HOD

objectively with multiple – factor fuzzy time series, recurrent number of fuzzy relationships, and assigning weights to elements of fuzzy forecasting rules. In this paper, a novel two – factor high – order fuzzy time series forecasting method based on fuzzy C-means clustering and particle swarm optimization is proposed to ...

Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach

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E. Golden Julie

2016-01-01

Full Text Available Wireless sensor networks (WSNs consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.

Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks

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Teng Gao

2015-01-01

Full Text Available Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms.

The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data

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Husein, A. M.; Harahap, M.; Aisyah, S.; Purba, W.; Muhazir, A.

2018-03-01

Medication planning aim to get types, amount of medicine according to needs, and avoid the emptiness medicine based on patterns of disease. In making the medicine planning is still rely on ability and leadership experience, this is due to take a long time, skill, difficult to obtain a definite disease data, need a good record keeping and reporting, and the dependence of the budget resulted in planning is not going well, and lead to frequent lack and excess of medicines. In this research, we propose Adaptive Neuro Fuzzy Inference System (ANFIS) method to predict medication needs in 2016 and 2017 based on medical data in 2015 and 2016 from two source of hospital. The framework of analysis using two approaches. The first phase is implementing ANFIS to a data source, while the second approach we keep using ANFIS, but after the process of clustering from K-Means algorithm, both approaches are calculated values of Root Mean Square Error (RMSE) for training and testing. From the testing result, the proposed method with better prediction rates based on the evaluation analysis of quantitative and qualitative compared with existing systems, however the implementation of K-Means Algorithm against ANFIS have an effect on the timing of the training process and provide a classification accuracy significantly better without clustering.

A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

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Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

2017-01-01

An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

Identification of different geologic units using fuzzy constrained resistivity tomography

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Singh, Anand; Sharma, S. P.

2018-01-01

Different geophysical inversion strategies are utilized as a component of an interpretation process that tries to separate geologic units based on the resistivity distribution. In the present study, we present the results of separating different geologic units using fuzzy constrained resistivity tomography. This was accomplished using fuzzy c means, a clustering procedure to improve the 2D resistivity image and geologic separation within the iterative minimization through inversion. First, we developed a Matlab-based inversion technique to obtain a reliable resistivity image using different geophysical data sets (electrical resistivity and electromagnetic data). Following this, the recovered resistivity model was converted into a fuzzy constrained resistivity model by assigning the highest probability value of each model cell to the cluster utilizing fuzzy c means clustering procedure during the iterative process. The efficacy of the algorithm is demonstrated using three synthetic plane wave electromagnetic data sets and one electrical resistivity field dataset. The presented approach shows improvement on the conventional inversion approach to differentiate between different geologic units if the correct number of geologic units will be identified. Further, fuzzy constrained resistivity tomography was performed to examine the augmentation of uranium mineralization in the Beldih open cast mine as a case study. We also compared geologic units identified by fuzzy constrained resistivity tomography with geologic units interpreted from the borehole information.

Fracture enhancement based on artificial ants and fuzzy c-means clustering (FCMC) in Dezful Embayment of Iran

International Nuclear Information System (INIS)

Nasseri, Aynur; Mohammadzadeh, Mohammad Jafar; Tabatabaei Raeisi, S Hashem

2015-01-01

This paper deals with the application of the ant colony algorithm (AC) to a seismic dataset from Dezful Embayment in the southwest region of Iran. The objective of the approach is to generate an accurate representation of faults and discontinuities to assist in pertinent matters such as well planning and field optimization. The AC analyzed all spatial discontinuities in the seismic attributes from which features were extracted. True fault information from the attributes was detected by many artificial ants, whereas noise and the remains of the reflectors were eliminated. Furthermore, the fracture enhancement procedure was conducted by three steps on seismic data of the area. In the first step several attributes such as chaos, variance/coherence and dip deviation were taken into account; the resulting maps indicate high-resolution contrast for the variance attribute. Subsequently, the enhancement of spatial discontinuities was performed and finally elimination of the noise and remains of non-faulting events was carried out by simulating the behavior of ant colonies. After considering stepwise attribute optimization, focusing on chaos and variance in particular, an attribute fusion was generated and used in the ant colony algorithm. The resulting map displayed the highest performance in feature detection along the main structural feature trend, confined to a NW–SE direction. Thus, the optimized attribute fusion might be used with greater confidence to map the structural feature network with more accuracy and resolution. In order to assess the performance of the AC in feature detection, and cross validate the reliability of the method used, fuzzy c-means clustering (FCMC) was employed for the same dataset. Comparing the maps illustrates the effectiveness and preference of the AC approach due to its high resolution contrast for structural feature detection compared to the FCMC method. Accordingly, 3D planes of discontinuity determined spatial distribution of

Collaborative filtering recommendation model based on fuzzy clustering algorithm

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Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering

International Nuclear Information System (INIS)

Acton, P.D.; Pilowsky, L.S.; Kung, H.F.; Ell, P.J.

1999-01-01

The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D 2 /D 3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [ 123 I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r 2 >0.99, P 123 I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r 2 =0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner. (orig.)

AUTOMATIC EXTRACTION OF ROCK JOINTS FROM LASER SCANNED DATA BY MOVING LEAST SQUARES METHOD AND FUZZY K-MEANS CLUSTERING

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S. Oh

2012-09-01

Full Text Available Recent development of laser scanning device increased the capability of representing rock outcrop in a very high resolution. Accurate 3D point cloud model with rock joint information can help geologist to estimate stability of rock slope on-site or off-site. An automatic plane extraction method was developed by computing normal directions and grouping them in similar direction. Point normal was calculated by moving least squares (MLS method considering every point within a given distance to minimize error to the fitting plane. Normal directions were classified into a number of dominating clusters by fuzzy K-means clustering. Region growing approach was exploited to discriminate joints in a point cloud. Overall procedure was applied to point cloud with about 120,000 points, and successfully extracted joints with joint information. The extraction procedure was implemented to minimize number of input parameters and to construct plane information into the existing point cloud for less redundancy and high usability of the point cloud itself.

Unsupervised seismic facies analysis with spatial constraints using regularized fuzzy c-means

Science.gov (United States)

Song, Chengyun; Liu, Zhining; Cai, Hanpeng; Wang, Yaojun; Li, Xingming; Hu, Guangmin

2017-12-01

Seismic facies analysis techniques combine classification algorithms and seismic attributes to generate a map that describes main reservoir heterogeneities. However, most of the current classification algorithms only view the seismic attributes as isolated data regardless of their spatial locations, and the resulting map is generally sensitive to noise. In this paper, a regularized fuzzy c-means (RegFCM) algorithm is used for unsupervised seismic facies analysis. Due to the regularized term of the RegFCM algorithm, the data whose adjacent locations belong to same classification will play a more important role in the iterative process than other data. Therefore, this method can reduce the effect of seismic data noise presented in discontinuous regions. The synthetic data with different signal/noise values are used to demonstrate the noise tolerance ability of the RegFCM algorithm. Meanwhile, the fuzzy factor, the neighbour window size and the regularized weight are tested using various values, to provide a reference of how to set these parameters. The new approach is also applied to a real seismic data set from the F3 block of the Netherlands. The results show improved spatial continuity, with clear facies boundaries and channel morphology, which reveals that the method is an effective seismic facies analysis tool.

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

International Nuclear Information System (INIS)

Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard

2014-01-01

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

Energy Technology Data Exchange (ETDEWEB)

Nedialkova, Lilia V.; Amat, Miguel A. [Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544 (United States); Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544 (United States); Hummer, Gerhard, E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438 Frankfurt am Main (Germany)

2014-09-21

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

Science.gov (United States)

Girola Schneider, R.

2017-07-01

The fuzzy logic is a branch of the artificial intelligence founded on the concept that everything is a matter of degree. It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters. Fuzzy logic enables the researcher to work with "imprecise" information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic's techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

Classifying Aerosols Based on Fuzzy Clustering and Their Optical and Microphysical Properties Study in Beijing, China

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Wenhao Zhang

2017-01-01

Full Text Available Classification of Beijing aerosol is carried out based on clustering optical properties obtained from three Aerosol Robotic Network (AERONET sites. The fuzzy c-mean (FCM clustering algorithm is used to classify fourteen-year (2001–2014 observations, totally of 6,732 records, into six aerosol types. They are identified as fine particle nonabsorbing, two kinds of fine particle moderately absorbing (fine-MA1 and fine-MA2, fine particle highly absorbing, polluted dust, and desert dust aerosol. These aerosol types exhibit obvious optical characteristics difference. While five of them show similarities with aerosol types identified elsewhere, the polluted dust aerosol has no comparable prototype. Then the membership degree, a significant parameter provided by fuzzy clustering, is used to analyze internal variation of optical properties of each aerosol type. Finally, temporal variations of aerosol types are investigated. The dominant aerosol types are polluted dust and desert dust in spring, fine particle nonabsorbing aerosol in summer, and fine particle highly absorbing aerosol in winter. The fine particle moderately absorbing aerosol occurs during the whole year. Optical properties of the six types can also be used for radiative forcing estimation and satellite aerosol retrieval. Additionally, methodology of this study can be applied to identify aerosol types on a global scale.

Covering Image Segmentation via Matrix X-means and J-means Clustering

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Volodymyr MASHTALIR

2015-12-01

Full Text Available To provide tools for image understanding, non-trivial task of image segmentation is now put on a new semantic level of object detection. Internal, external and contextual region properties often can adequately represent image content but there arises field of view coverings due shape ambiguities on blurred images. Truthful image interpretation strictly depends on valid number of regions. The goal is an attempt to solve image clustering problem under fuzzy conditions of overlapping classes, more specifically, to find estimation of meaningful region number with following refining of fuzzy clustering data in matrix form.

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

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Jean Marie Vianney Kinani

2017-01-01

Full Text Available We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient’s response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR and fluid-attenuated inversion recovery (FLAIR images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

A possibilistic approach to clustering

Science.gov (United States)

Krishnapuram, Raghu; Keller, James M.

1993-01-01

Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data

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Yubo Jiao

2013-01-01

Full Text Available Performance evaluation of a bridge is critical for determining the optimal maintenance strategy. An unsupervised bridge superstructure state assessment method is proposed in this paper based on fuzzy clustering and bridge field measured data. Firstly, the evaluation index system of bridge is constructed. Secondly, a certain number of bridge health monitoring data are selected as clustering samples to obtain the fuzzy similarity matrix and fuzzy equivalent matrix. Finally, different thresholds are selected to form dynamic clustering maps and determine the best classification based on statistic analysis. The clustering result is regarded as a sample base, and the bridge state can be evaluated by calculating the fuzzy nearness between the unknown bridge state data and the sample base. Nanping Bridge in Jilin Province is selected as the engineering project to verify the effectiveness of the proposed method.

Segmentation of pomegranate MR images using spatial fuzzy c-means (SFCM) algorithm

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Moradi, Ghobad; Shamsi, Mousa; Sedaaghi, M. H.; Alsharif, M. R.

2011-10-01

Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. Fuzzy c-means (FCM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FCM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FCM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFCM algorithm operates much more significantly than FCM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFCM algorithm with 5×5 window size is better than the other windows.

Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

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Jocelyn H Bolin

2014-04-01

Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

Science.gov (United States)

Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

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Ozge Cagcag Yolcu

2013-01-01

Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

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Zhou, Shuguang; Zhou, Kefa; Wang, Jinlin; Yang, Genfang; Wang, Shanshan

2017-12-01

Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms (the hierarchical, k-means, and fuzzy c-means algorithms) and six data transformation methods (the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT) are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of column- or variable-based (R-type) cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based (Q-type) cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy c-means clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange

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Jahangoshai Rezaee, Mustafa; Jozmaleki, Mehrdad; Valipour, Mahsa

2018-01-01

One of the main features to invest in stock exchange companies is their financial performance. On the other hand, conventional evaluation methods such as data envelopment analysis are not only a retrospective process, but are also a process, which are incomplete and ineffective approaches to evaluate the companies in the future. To remove this problem, it is required to plan an expert system for evaluating organizations when the online data are received from stock exchange market. This paper deals with an approach for predicting the online financial performance of companies when data are received in different time's intervals. The proposed approach is based on integrating fuzzy C-means (FCM), data envelopment analysis (DEA) and artificial neural network (ANN). The classical FCM method is unable to update the number of clusters and their members when the data are changed or the new data are received. Hence, this method is developed in order to make dynamic features for the number of clusters and clusters members in classical FCM. Then, DEA is used to evaluate DMUs by using financial ratios to provide targets in neural network. Finally, the designed network is trained and prepared for predicting companies' future performance. The data on Tehran Stock Market companies for six consecutive years (2007-2012) are used to show the abilities of the proposed approach.

Fuzzy Nonnative Phonolexical Representations Lead to Fuzzy Form-to-Meaning Mappings

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Svetlana V Cook

2016-09-01

Full Text Available The present paper explores nonnative (L2 phonological encoding of lexical entries and dissociates the difficulties associated with L2 phonological and phonolexical encoding by focusing on similarly sounding L2 words that are not differentiated by difficult phonological contrasts. We test two main claims of the fuzzy lexicon hypothesis: (1 L2 fuzzy phonolexical representations are not fully specified and lack details at both phonological and phonolexical levels of representation (Experiment 1; and (2 fuzzy phonolexical representations can lead to establishing incorrect form-to-meaning mappings (Experiment 2.The Russian-English Translation Priming task (Experiment 1, TJT explores how the degree of phonolexical similarity between a word and its lexical competitor affects lexical access of Russian words. Words with smaller phonolexical distance (e.g., parent - parrot show longer reaction times and lower accuracy compared to words with a larger phonolexical distance (e.g., parent – parchment in lower-proficiency nonnative speakers, and, to a lesser degree, higher-proficiency speakers. This points to a lack of detail in nonnative phonolexical representations necessary for efficient lexical access. The Russian Pseudo-Semantic Priming task (Experiment 2, PSP addresses the vulnerability of form-to-meaning mappings as a consequence of fuzzy phonolexical representations in L2. We primed the target with a word semantically related to its phonological competitor, or a potentially confusable word. The findings of Experiment 2 extend the results of Experiment 1 that, unlike native speakers, nonnative speakers do not properly encode phonolexical information. As a result, they are prone to access an incorrect lexical representation of a competitor word, as indicated by a slowdown in the judgments to confusable words.The study provides evidence that fuzzy phonolexical representations result in unfaithful form-to-meaning mappings, which lead to retrieval of

Design of a Fuzzy Rule Base Expert System to Predict and Classify ...

African Journals Online (AJOL)

The main objective of design of a rule base expert system using fuzzy logic approach is to predict and forecast the risk level of cardiac patients to avoid sudden death. In this proposed system, uncertainty is captured using rule base and classification using fuzzy c-means clustering is discussed to overcome the risk level, ...

A fourth order PDE based fuzzy c- means approach for segmentation of microscopic biopsy images in presence of Poisson noise for cancer detection.

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Kumar, Rajesh; Srivastava, Subodh; Srivastava, Rajeev

2017-07-01

For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in the image the segmentation results may not be accurate. The objective is to propose an efficient fuzzy c-means based segmentation approach which can also handle the noise present in the image during the segmentation process itself i.e. noise removal and segmentation is combined in one step. To address the above issues, in this paper a fourth order partial differential equation (FPDE) based nonlinear filter adapted to Poisson noise with fuzzy c-means segmentation method is proposed. This approach is capable of effectively handling the segmentation problem of blocky artifacts while achieving good tradeoff between Poisson noise removals and edge preservation of the microscopic biopsy images during segmentation process for cancer detection from cells. The proposed approach is tested on breast cancer microscopic biopsy data set with region of interest (ROI) segmented ground truth images. The microscopic biopsy data set contains 31 benign and 27 malignant images of size 896 × 768. The region of interest selected ground truth of all 58 images are also available for this data set. Finally, the result obtained from proposed approach is compared with the results of popular segmentation algorithms; fuzzy c-means, color k-means, texture based segmentation, and total variation fuzzy c-means approaches. The experimental results shows that proposed approach is providing better results in terms of various performance measures such as Jaccard coefficient, dice index, Tanimoto coefficient, area under curve, accuracy, true positive rate, true negative rate, false positive rate, false negative rate, random index, global consistency error, and variance of information as compared to other

Data-driven modeling and predictive control for boiler-turbine unit using fuzzy clustering and subspace methods.

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Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y

2014-05-01

This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Study on distinguishing of Chinese ancient porcelains by neutron activation and fuzzy cluster analysis

International Nuclear Information System (INIS)

Wang An

1992-01-01

By means of the method of neutron activation, the contents of trace elements in some samples of Chinese ancient porcelains from different places of production were determined. The data were analysed by fuzzy cluster analysis. On the basis of the above mentioned works, a method with regard to the distinguishing and determining of Chinese ancient porcelain was suggested

Risk Assessment for Bridges Safety Management during Operation Based on Fuzzy Clustering Algorithm

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Xia Hanyu

2016-01-01

Full Text Available In recent years, large span and large sea-crossing bridges are built, bridges accidents caused by improper operational management occur frequently. In order to explore the better methods for risk assessment of the bridges operation departments, the method based on fuzzy clustering algorithm is selected. Then, the implementation steps of fuzzy clustering algorithm are described, the risk evaluation system is built, and Taizhou Bridge is selected as an example, the quantitation of risk factors is described. After that, the clustering algorithm based on fuzzy equivalence is calculated on MATLAB 2010a. In the last, Taizhou Bridge operation management departments are classified and sorted according to the degree of risk, and the safety situation of operation departments is analyzed.

A REVIEW WAVELET TRANSFORM AND FUZZY K-MEANS BASED IMAGE DE-NOISING METHOD

OpenAIRE

Nidhi Patel*, Asst. Prof. Pratik Kumar Soni

2017-01-01

The research area of image processing technique using fuzzy k-means and wavelet transform. The enormous amount of data necessary for images is a main reason for the growth of many areas within the research field of computer imaging such as image processing and compression. In order to get this in requisites of the concerned research work, wavelet transforms and k-means clustering is applied. This can be done in order to discover more possible combinations that may lead to the finest de-noisin...

Characterization and prediction of the backscattered form function of an immersed cylindrical shell using hybrid fuzzy clustering and bio-inspired algorithms.

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Agounad, Said; Aassif, El Houcein; Khandouch, Younes; Maze, Gérard; Décultot, Dominique

2018-02-01

The acoustic scattering of a plane wave by an elastic cylindrical shell is studied. A new approach is developed to predict the form function of an immersed cylindrical shell of the radius ratio b/a ('b' is the inner radius and 'a' is the outer radius). The prediction of the backscattered form function is investigated by a combined approach between fuzzy clustering algorithms and bio-inspired algorithms. Four famous fuzzy clustering algorithms: the fuzzy c-means (FCM), the Gustafson-Kessel algorithm (GK), the fuzzy c-regression model (FCRM) and the Gath-Geva algorithm (GG) are combined with particle swarm optimization and genetic algorithm. The symmetric and antisymmetric circumferential waves A, S 0 , A 1 , S 1 and S 2 are investigated in a reduced frequency (k 1 a) range extends over 0.1

Ellipsoidal fuzzy learning for smart car platoons

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Dickerson, Julie A.; Kosko, Bart

1993-12-01

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

A Geometric Fuzzy-Based Approach for Airport Clustering

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Maria Nadia Postorino

2014-01-01

Full Text Available Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations.

Application of Fuzzy Clustering in Modeling of a Water Hydraulics System

DEFF Research Database (Denmark)

Zhou, Jianjun; Kroszynski, Uri

2000-01-01

This article presents a case study of applying fuzzy modeling techniques for a water hydraulics system. The obtained model is intended to provide a basis for model-based control of the system. Fuzzy clustering is used for classifying measured input-output data points into partitions. The fuzzy...... model is extracted from the obtained partitions. The identified model has been evaluated by comparing measurements with simulation results. The evaluation shows that the identified model is capable of describing the system dynamics over a reasonably wide frequency range....

Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model

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Li, X. L.; Zhao, Q. H.; Li, Y.

2017-09-01

Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.

Implementation of Automatic Clustering Algorithm and Fuzzy Time Series in Motorcycle Sales Forecasting

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Rasim; Junaeti, E.; Wirantika, R.

2018-01-01

Accurate forecasting for the sale of a product depends on the forecasting method used. The purpose of this research is to build motorcycle sales forecasting application using Fuzzy Time Series method combined with interval determination using automatic clustering algorithm. Forecasting is done using the sales data of motorcycle sales in the last ten years. Then the error rate of forecasting is measured using Means Percentage Error (MPE) and Means Absolute Percentage Error (MAPE). The results of forecasting in the one-year period obtained in this study are included in good accuracy.

A Hybrid Fuzzy Multi-hop Unequal Clustering Algorithm for Dense Wireless Sensor Networks

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Shawkat K. Guirguis

2017-01-01

Full Text Available Clustering is carried out to explore and solve power dissipation problem in wireless sensor network (WSN. Hierarchical network architecture, based on clustering, can reduce energy consumption, balance traffic load, improve scalability, and prolong network lifetime. However, clustering faces two main challenges: hotspot problem and searching for effective techniques to perform clustering. This paper introduces a fuzzy unequal clustering technique for heterogeneous dense WSNs to determine both final cluster heads and their radii. Proposed fuzzy system blends three effective parameters together which are: the distance to the base station, the density of the cluster, and the deviation of the noders residual energy from the average network energy. Our objectives are achieving gain for network lifetime, energy distribution, and energy consumption. To evaluate the proposed algorithm, WSN clustering based routing algorithms are analyzed, simulated, and compared with obtained results. These protocols are LEACH, SEP, HEED, EEUC, and MOFCA.

Adding-point strategy for reduced-order hypersonic aerothermodynamics modeling based on fuzzy clustering

Science.gov (United States)

Chen, Xin; Liu, Li; Zhou, Sida; Yue, Zhenjiang

2016-09-01

Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS

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Jani Kusanti

2015-07-01

Abstract             The use of Adaptive Neuro Fuzzy Inference System (ANFIS methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS. Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66%   Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE

Hybrid clustering based fuzzy structure for vibration control - Part 1: A novel algorithm for building neuro-fuzzy system

Science.gov (United States)

Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

2015-01-01

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, 'Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.

Intelligent Technique for Signal Processing to Identify the Brain Disorder for Epilepsy Captures Using Fuzzy Systems

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Gurumurthy Sasikumar

2016-01-01

Full Text Available The new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of the extreme prevalent and then programmed artificial intelligence detection technique is an essential due to the crooked and unpredictable nature of happening of epileptic seizures. We proposed an Improved Fuzzy firefly algorithm, which would enhance the classification of the brain signal efficiently with minimum iteration. An important bunching technique created on fuzzy logic is the Fuzzy C means. Together in the feature domain with the spatial domain the features gained after multichannel EEG signals remained combined by means of fuzzy algorithms. And for better precision segmentation process the firefly algorithm is applied to optimize the Fuzzy C-means membership function. Simultaneously for the efficient clustering method the convergence criteria are set. On the whole the proposed technique yields more accurate results and that gives an edge over other techniques. This proposed algorithm result compared with other algorithms like fuzzy c means algorithm and PSO algorithm.

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

Science.gov (United States)

Kim, Chan Moon; Parnichkun, Manukid

2017-11-01

Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

Science.gov (United States)

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.

Science.gov (United States)

Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong

2017-07-03

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

A semi-supervised method to detect seismic random noise with fuzzy GK clustering

International Nuclear Information System (INIS)

Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert

2008-01-01

We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

International Nuclear Information System (INIS)

Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina

2012-01-01

Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., “FOR PROCESSING”) and vendor postprocessed (i.e., “FOR PRESENTATION”), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which

Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation

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Ricardo FAIA

2016-10-01

Full Text Available Artificial Intelligence (AI methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM. In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.

Two generalizations of Kohonen clustering

Science.gov (United States)

Bezdek, James C.; Pal, Nikhil R.; Tsao, Eric C. K.

1993-01-01

The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ.

Effect of Deep Cryogenic treatment on AISI A8 Tool steel & Development of Wear Mechanism maps using Fuzzy Clustering

Science.gov (United States)

Pillai, Nandakumar; Karthikeyan, R., Dr.

2018-04-01

Tool steels are widely classified according to their constituents and type of thermal treatments carried out to obtain its properties. Viking a special purpose tool steel coming under AISI A8 cold working steel classification is widely used for heavy duty blanking and forming operations. The optimum combination of wear resistance and toughness as well as ease of machinability in pre-treated condition makes this material accepted in heavy cutting and non cutting tool manufacture. Air or vacuum hardening is recommended as the normal treatment procedure to obtain the desired mechanical and tribological properties for steels under this category. In this study, we are incorporating a deep cryogenic phase within the conventional treatment cycle both before and after tempering. The thermal treatments at sub zero temperatures up to -195°C using cryogenic chamber with liquid nitrogen as medium was conducted. Micro structural changes in its microstructure and the corresponding improvement in the tribological and physical properties are analyzed. The cryogenic treatment leads to more conversion of retained austenite to martensite and also formation of fine secondary carbides. The microstructure is studied using the micrographs taken using optical microscopy. The wear tests are conducted on DUCOM tribometer for different combinations of speed and load under normal temperature. The wear rates and coefficient of friction obtained from these experiments are used to developed wear mechanism maps with the help of fuzzy c means clustering and probabilistic neural network models. Fuzzy C means clustering is an effective algorithm to group data of similar patterns. The wear mechanisms obtained from the computationally developed maps are then compared with the SEM photographs taken and the improvement in properties due to this additional cryogenic treatment is validated.

Brain tumor segmentation based on a hybrid clustering technique

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Eman Abdel-Maksoud

2015-03-01

This paper presents an efficient image segmentation approach using K-means clustering technique integrated with Fuzzy C-means algorithm. It is followed by thresholding and level set segmentation stages to provide an accurate brain tumor detection. The proposed technique can get benefits of the K-means clustering for image segmentation in the aspects of minimal computation time. In addition, it can get advantages of the Fuzzy C-means in the aspects of accuracy. The performance of the proposed image segmentation approach was evaluated by comparing it with some state of the art segmentation algorithms in case of accuracy, processing time, and performance. The accuracy was evaluated by comparing the results with the ground truth of each processed image. The experimental results clarify the effectiveness of our proposed approach to deal with a higher number of segmentation problems via improving the segmentation quality and accuracy in minimal execution time.

Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm.

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Gaber, Tarek; Ismail, Gehad; Anter, Ahmed; Soliman, Mona; Ali, Mona; Semary, Noura; Hassanien, Aboul Ella; Snasel, Vaclav

2015-08-01

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.

ADAPTIVE WEB SITE DENGAN METODE FUZZY CLUSTERING

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Muchammad Husni

2004-01-01

Full Text Available Normal 0 false false false IN X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Ledakan pertumbuhan dan perkembangan informasi dalam dunia maya menjadikan personalisasian informasi menjadi isu yang penting. Personalisasi informasi yang akan diberikan oleh situs web akan sangat mempengaruhi pola dan perilaku pengguna dalam pencarian informasi, terutama pada perdagangan elektronis (e-commerce. Salah satu pendekatan yang memungkinkan dalam personalisasian web adalah mencari profil pengguna (user profile dari data historis yang sangat besar di file web log. Pengklasifikasian data tanpa pengawasan (unsupervised clasification atau metode metode clustering cukup baik untuk menganalisa data log akses pengguna yang semi terstruktur. Pada metode ini, didefinisikan "user session" dan juga ukuran perbedaan (dissimilarity diantara dua web session yang menggambarkan pengorganisasian sebuah web site. Untuk mendapatkan sebuah profil akses pengguna, dilakukan pembagian user session berdasarkan pasangan ketidaksamaan menggunakan algoritma Fuzzy Clustering. Kata kunci : Adaptive Website, Fuzzy Clustering, personalisasi informasi.

Forecasting Jakarta composite index (IHSG) based on chen fuzzy time series and firefly clustering algorithm

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Ningrum, R. W.; Surarso, B.; Farikhin; Safarudin, Y. M.

2018-03-01

This paper proposes the combination of Firefly Algorithm (FA) and Chen Fuzzy Time Series Forecasting. Most of the existing fuzzy forecasting methods based on fuzzy time series use the static length of intervals. Therefore, we apply an artificial intelligence, i.e., Firefly Algorithm (FA) to set non-stationary length of intervals for each cluster on Chen Method. The method is evaluated by applying on the Jakarta Composite Index (IHSG) and compare with classical Chen Fuzzy Time Series Forecasting. Its performance verified through simulation using Matlab.

CAF: Cluster algorithm and a-star with fuzzy approach for lifetime enhancement in wireless sensor networks

KAUST Repository

Yuan, Y.; Li, C.; Yang, Y.; Zhang, Xiangliang; Li, L.

2014-01-01

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria. 2014 Yali Yuan et al.

CAF: Cluster algorithm and a-star with fuzzy approach for lifetime enhancement in wireless sensor networks

KAUST Repository

Yuan, Y.

2014-04-28

Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria. 2014 Yali Yuan et al.

Fuzzy forecasting based on fuzzy-trend logical relationship groups.

Science.gov (United States)

Chen, Shyi-Ming; Wang, Nai-Yi

2010-10-01

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

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Yuxian Zhang

2015-01-01

Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

Digital pulse shape discrimination of detector data using fuzzy clustering

International Nuclear Information System (INIS)

Kumar, Abhinav; Chatterjee, A.; Ramachandran, K.; Shrivastava, A.; Mahata, K.

2011-01-01

In accelerator based experiments, data acquisition is done by CAMAC, VME and other systems. The current trend is to digitize the pulse shapes and not just the peak heights of all the input channels, by means of Flash ADCs. In view of the large number of channels involved, this leads to unprecedented data volumes. Therefore, attempts to perform a first level of analysis in real time using algorithms implemented in FPGA have become important. In the present work, digital pulse shape discrimination using fuzzy clustering has been investigated. The attempt has been to devise general purpose PSD Techniques, loosely coupled with the characteristics of detector or particle type, for particle identification. The method is applicable to neutron-gamma discrimination for liquid scintillators and charged particles detected by Si detectors

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

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Zhi-Yong Li

2015-10-01

Full Text Available As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses.

Subspace K-means clustering.

Science.gov (United States)

Timmerman, Marieke E; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

2013-12-01

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic, stochastic, and unsupervised learning approaches. To evaluate subspace K-means, we performed a comparative simulation study, in which we manipulated the overlap of subspaces, the between-cluster variance, and the error variance. The study shows that the subspace K-means algorithm is sensitive to local minima but that the problem can be reasonably dealt with by using partitions of various cluster procedures as a starting point for the algorithm. Subspace K-means performs very well in recovering the true clustering across all conditions considered and appears to be superior to its competitor methods: K-means, reduced K-means, factorial K-means, mixtures of factor analyzers (MFA), and MCLUST. The best competitor method, MFA, showed a performance similar to that of subspace K-means in easy conditions but deteriorated in more difficult ones. Using data from a study on parental behavior, we show that subspace K-means analysis provides a rich insight into the cluster characteristics, in terms of both the relative positions of the clusters (via the centroids) and the shape of the clusters (via the within-cluster residuals).

A fuzzy inventory model with acceptable shortage using graded mean integration value method

Science.gov (United States)

Saranya, R.; Varadarajan, R.

2018-04-01

In many inventory models uncertainty is due to fuzziness and fuzziness is the closed possible approach to reality. In this paper, we proposed a fuzzy inventory model with acceptable shortage which is completely backlogged. We fuzzily the carrying cost, backorder cost and ordering cost using Triangular and Trapezoidal fuzzy numbers to obtain the fuzzy total cost. The purpose of our study is to defuzzify the total profit function by Graded Mean Integration Value Method. Further a numerical example is also given to demonstrate the developed crisp and fuzzy models.

Proposed Fuzzy-NN Algorithm with LoRaCommunication Protocol for Clustered Irrigation Systems

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Sotirios Kontogiannis

2017-11-01

Full Text Available Modern irrigation systems utilize sensors and actuators, interconnected together as a single entity. In such entities, A.I. algorithms are implemented, which are responsible for the irrigation process. In this paper, the authors present an irrigation Open Watering System (OWS architecture that spatially clusters the irrigation process into autonomous irrigation sections. Authors’ OWS implementation includes a Neuro-Fuzzy decision algorithm called FITRA, which originates from the Greek word for seed. In this paper, the FITRA algorithm is described in detail, as are experimentation results that indicate significant water conservations from the use of the FITRA algorithm. Furthermore, the authors propose a new communication protocol over LoRa radio as an alternative low-energy and long-range OWS clusters communication mechanism. The experimental scenarios confirm that the FITRA algorithm provides more efficient irrigation on clustered areas than existing non-clustered, time scheduled or threshold adaptive algorithms. This is due to the FITRA algorithm’s frequent monitoring of environmental conditions, fuzzy and neural network adaptation as well as adherence to past irrigation preferences.

Proposed Network Intrusion Detection System ‎Based on Fuzzy c Mean Algorithm in Cloud ‎Computing Environment

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Shawq Malik Mehibs

2017-12-01

Full Text Available Nowadays cloud computing had become is an integral part of IT industry, cloud computing provides Working environment allow a user of environmental to share data and resources over the internet. Where cloud computing its virtual grouping of resources offered over the internet, this lead to different matters related to the security and privacy in cloud computing. And therefore, create intrusion detection very important to detect outsider and insider intruders of cloud computing with high detection rate and low false positive alarm in the cloud environment. This work proposed network intrusion detection module using fuzzy c mean algorithm. The kdd99 dataset used for experiments .the proposed system characterized by a high detection rate with low false positive alarm

A combined approach for the enhancement and segmentation of mammograms using modified fuzzy C-means method in wavelet domain.

Science.gov (United States)

Srivastava, Subodh; Sharma, Neeraj; Singh, S K; Srivastava, R

2014-07-01

In this paper, a combined approach for enhancement and segmentation of mammograms is proposed. In preprocessing stage, a contrast limited adaptive histogram equalization (CLAHE) method is applied to obtain the better contrast mammograms. After this, the proposed combined methods are applied. In the first step of the proposed approach, a two dimensional (2D) discrete wavelet transform (DWT) is applied to all the input images. In the second step, a proposed nonlinear complex diffusion based unsharp masking and crispening method is applied on the approximation coefficients of the wavelet transformed images to further highlight the abnormalities such as micro-calcifications, tumours, etc., to reduce the false positives (FPs). Thirdly, a modified fuzzy c-means (FCM) segmentation method is applied on the output of the second step. In the modified FCM method, the mutual information is proposed as a similarity measure in place of conventional Euclidian distance based dissimilarity measure for FCM segmentation. Finally, the inverse 2D-DWT is applied. The efficacy of the proposed unsharp masking and crispening method for image enhancement is evaluated in terms of signal-to-noise ratio (SNR) and that of the proposed segmentation method is evaluated in terms of random index (RI), global consistency error (GCE), and variation of information (VoI). The performance of the proposed segmentation approach is compared with the other commonly used segmentation approaches such as Otsu's thresholding, texture based, k-means, and FCM clustering as well as thresholding. From the obtained results, it is observed that the proposed segmentation approach performs better and takes lesser processing time in comparison to the standard FCM and other segmentation methods in consideration.

Intuitionistic Fuzzy Normalized Weighted Bonferroni Mean and Its Application in Multicriteria Decision Making

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Wei Zhou

2012-01-01

Full Text Available The Bonferroni mean (BM was introduced by Bonferroni six decades ago but has been a hot research topic recently since its usefulness of the aggregation techniques. The desirable characteristic of the BM is its capability to capture the interrelationship between input arguments. However, the classical BM and GBM ignore the weight vector of aggregated arguments, the general weighted BM (WBM has not the reducibility, and the revised generalized weighted BM (GWBM cannot reflect the interrelationship between the individual criterion and other criteria. To deal with these issues, in this paper, we propose the normalized weighted Bonferroni mean (NWBM and the generalized normalized weighted Bonferroni mean (GNWBM and study their desirable properties, such as reducibility, idempotency, monotonicity, and boundedness. Furthermore, we investigate the NWBM and GNWBM operators under the intuitionistic fuzzy environment which is more common phenomenon in modern life and develop two new intuitionistic fuzzy aggregation operators based on the NWBM and GNWBM, that is, the intuitionistic fuzzy normalized weighted Bonferroni mean (IFNWBM and the generalized intuitionistic fuzzy normalized weighted Bonferroni mean (GIFNWBM. Finally, based on the GIFNWBM, we propose an approach to multicriteria decision making under the intuitionistic fuzzy environment, and a practical example is provided to illustrate our results.

Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.

Science.gov (United States)

Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

2013-05-01

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

Energy Technology Data Exchange (ETDEWEB)

Keller, Brad M.; Nathan, Diane L.; Wang Yan; Zheng Yuanjie; Gee, James C.; Conant, Emily F.; Kontos, Despina [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)

2012-08-15

Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') and vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely

FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN

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M R Sumathi

2017-04-01

Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.

Cluster Analysis of Customer Reviews Extracted from Web Pages

Directory of Open Access Journals (Sweden)

S. Shivashankar

2010-01-01

Full Text Available As e-commerce is gaining popularity day by day, the web has become an excellent source for gathering customer reviews / opinions by the market researchers. The number of customer reviews that a product receives is growing at very fast rate (It could be in hundreds or thousands. Customer reviews posted on the websites vary greatly in quality. The potential customer has to read necessarily all the reviews irrespective of their quality to make a decision on whether to purchase the product or not. In this paper, we make an attempt to assess are view based on its quality, to help the customer make a proper buying decision. The quality of customer review is assessed as most significant, more significant, significant and insignificant.A novel and effective web mining technique is proposed for assessing a customer review of a particular product based on the feature clustering techniques, namely, k-means method and fuzzy c-means method. This is performed in three steps : (1Identify review regions and extract reviews from it, (2 Extract and cluster the features of reviews by a clustering technique and then assign weights to the features belonging to each of the clusters (groups and (3 Assess the review by considering the feature weights and group belongingness. The k-means and fuzzy c-means clustering techniques are implemented and tested on customer reviews extracted from web pages. Performance of these techniques are analyzed.

FRCA: A Fuzzy Relevance-Based Cluster Head Selection Algorithm for Wireless Mobile Ad-Hoc Sensor Networks

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Taegwon Jeong

2011-05-01

Full Text Available Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP, the Weighted-based Adaptive Clustering Algorithm (WACA, and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM. The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks.

Science.gov (United States)

Lee, Chongdeuk; Jeong, Taegwon

2011-01-01

Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.

A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

Science.gov (United States)

Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

2013-01-01

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

Directory of Open Access Journals (Sweden)

C. K. Kwong

2013-01-01

Full Text Available Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1 the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS failed to run due to a large number of inputs; (2 the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

Maclaurin symmetric mean operators of linguistic intuitionistic fuzzy numbers and their application to multiple-attribute decision-making

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Liu, Peide; Qin, Xiyou

2017-11-01

Linguistic intuitionistic fuzzy number (LIFN) is a special intuitionistic fuzzy number which can more easily describe the vagueness existing in the real decision-making. Maclaurin symmetric mean (MSM) operator has the characteristic of considering the interrelationships among any number of input parameters. In this paper, we extended the MSM operator to the LIFNs and some extended MSM operators for LIFNs were proposed, some new decision-making methods were developed. Firstly, we introduced the definition, score function, properties and operational rules of the LIFNs. Then, we proposed some linguistic intuitionistic fuzzy MSM operators, such as linguistic intuitionistic fuzzy Maclaurin symmetric mean operator, weighted linguistic intuitionistic fuzzy Maclaurin symmetric mean (WLIFMSM) operator, linguistic intuitionistic fuzzy dual Maclaurin symmetric mean operator, weighted linguistic intuitionistic fuzzy dual Maclaurin symmetric mean (WLIFDMSM) operator. In the meantime, we studied some important properties of these operators, and developed some methods based on WLIFMSM operator and WLIFDMSM operator for multi-attribute decision-making. Finally, we use an example to demonstrate the effectiveness of the proposed methods.

Hesitant fuzzy sets theory

CERN Document Server

Xu, Zeshui

2014-01-01

This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) chara...

Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs

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Theis Fabian J

2010-10-01

Full Text Available Abstract Background Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. Results Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. Conclusions In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning

An improved K-means clustering method for cDNA microarray image segmentation.

Science.gov (United States)

Wang, T N; Li, T J; Shao, G F; Wu, S X

2015-07-14

Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

a Novel 3d Intelligent Fuzzy Algorithm Based on Minkowski-Clustering

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Toori, S.; Esmaeily, A.

2017-09-01

Assessing and monitoring the state of the earth surface is a key requirement for global change research. In this paper, we propose a new consensus fuzzy clustering algorithm that is based on the Minkowski distance. This research concentrates on Tehran's vegetation mass and its changes during 29 years using remote sensing technology. The main purpose of this research is to evaluate the changes in vegetation mass using a new process by combination of intelligent NDVI fuzzy clustering and Minkowski distance operation. The dataset includes the images of Landsat8 and Landsat TM, from 1989 to 2016. For each year three images of three continuous days were used to identify vegetation impact and recovery. The result was a 3D NDVI image, with one dimension for each day NDVI. The next step was the classification procedure which is a complicated process of categorizing pixels into a finite number of separate classes, based on their data values. If a pixel satisfies a certain set of standards, the pixel is allocated to the class that corresponds to those criteria. This method is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. The result was a fuzzy one dimensional image. This image was also computed for the next 28 years. The classification was done in both specified urban and natural park areas of Tehran. Experiments showed that our method worked better in classifying image pixels in comparison with the standard classification methods.

Hierarchical modularization of biochemical pathways using fuzzy-c means clustering.

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de Luis Balaguer, Maria A; Williams, Cranos M

2014-08-01

Biological systems that are representative of regulatory, metabolic, or signaling pathways can be highly complex. Mathematical models that describe such systems inherit this complexity. As a result, these models can often fail to provide a path toward the intuitive comprehension of these systems. More coarse information that allows a perceptive insight of the system is sometimes needed in combination with the model to understand control hierarchies or lower level functional relationships. In this paper, we present a method to identify relationships between components of dynamic models of biochemical pathways that reside in different functional groups. We find primary relationships and secondary relationships. The secondary relationships reveal connections that are present in the system, which current techniques that only identify primary relationships are unable to show. We also identify how relationships between components dynamically change over time. This results in a method that provides the hierarchy of the relationships among components, which can help us to understand the low level functional structure of the system and to elucidate potential hierarchical control. As a proof of concept, we apply the algorithm to the epidermal growth factor signal transduction pathway, and to the C3 photosynthesis pathway. We identify primary relationships among components that are in agreement with previous computational decomposition studies, and identify secondary relationships that uncover connections among components that current computational approaches were unable to reveal.

Combined Forecasting of Rainfall Based on Fuzzy Clustering and Cross Entropy

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Baohui Men

2017-12-01

Full Text Available Rainfall is an essential index to measure drought, and it is dependent upon various parameters including geographical environment, air temperature and pressure. The nonlinear nature of climatic variables leads to problems such as poor accuracy and instability in traditional forecasting methods. In this paper, the combined forecasting method based on data mining technology and cross entropy is proposed to forecast the rainfall with full consideration of the time-effectiveness of historical data. In view of the flaws of the fuzzy clustering method which is easy to fall into local optimal solution and low speed of operation, the ant colony algorithm is adopted to overcome these shortcomings and, as a result, refine the model. The method for determining weights is also improved by using the cross entropy. Besides, the forecast is conducted by analyzing the weighted average rainfall based on Thiessen polygon in the Beijing–Tianjin–Hebei region. Since the predictive errors are calculated, the results show that improved ant colony fuzzy clustering can effectively select historical data and enhance the accuracy of prediction so that the damage caused by extreme weather events like droughts and floods can be greatly lessened and even kept at bay.

On Hesitant Fuzzy Reducible Weighted Bonferroni Mean and Its Generalized Form for Multicriteria Aggregation

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Wei Zhou

2014-01-01

Full Text Available Due to convenience and powerfulness in dealing with vagueness and uncertainty of real situation, hesitant fuzzy set has received more and more attention and has been a hot research topic recently. To differently process and effectively aggregate hesitant fuzzy information and capture their interrelationship, in this paper, we propose the hesitant fuzzy reducible weighted Bonferroni mean (HFRWBM and present its four prominent characteristics, namely, reductibility, monotonicity, boundedness, and idempotency. Then, we further investigate its generalized form, that is, the generalized hesitant fuzzy reducible weighted Bonferroni mean (GHFRWBM. Based on the discussion of model parameters, some special cases of the HFRWBM and GHFRWBM are studied in detail. In addition, to deal with the situation that multicriteria have connections in hesitant fuzzy information aggregation, a three-step aggregation approach has been proposed on the basis of the HFRWBM and GHFRWBM. In the end, we apply the proposed aggregation operators to multicriteria aggregation and give an example to illustrate our results.

Uncovering and testing the fuzzy clusters based on lumped Markov chain in complex network.

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Jing, Fan; Jianbin, Xie; Jinlong, Wang; Jinshuai, Qu

2013-01-01

Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

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Jinjun Tang

Full Text Available Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN, two learning processes are proposed: (1 a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2 a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE, root mean square error (RMSE, and mean absolute relative error (MARE are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR, instantaneous model (IM, linear model (LM, neural network (NN, and cumulative plots (CP.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

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Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Normalization based K means Clustering Algorithm

OpenAIRE

Virmani, Deepali; Taneja, Shweta; Malhotra, Geetika

2015-01-01

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing...

Pythagorean Fuzzy Muirhead Mean Operators and Their Application in Multiple-Criteria Group Decision-Making

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Jianghong Zhu

2018-06-01

Full Text Available As a generalization of the intuitionistic fuzzy set (IFS, a Pythagorean fuzzy set has more flexibility than IFS in expressing uncertainty and fuzziness in the process of multiple criteria group decision-making (MCGDM. Meanwhile, the prominent advantage of the Muirhead mean (MM operator is that it can reflect the relationships among the various input arguments through changing a parameter vector. Motivated by these primary characters, in this study, we introduced the MM operator into the Pythagorean fuzzy context to expand its applied fields. To do so, we presented the Pythagorean fuzzy MM (PFMM operators and Pythagorean fuzzy dual MM (PFDMM operator to fuse the Pythagorean fuzzy information. Then, we investigated their some properties and gave some special cases related to the parameter vector. In addition, based on the developed operators, two MCGDM methods under the Pythagorean fuzzy environment are proposed. An example is given to verify the validity and feasibility of our proposed methods, and a comparative analysis is provided to show their advantages.

Evaluation of B2C website based on the usability factors by using fuzzy AHP & hierarchical fuzzy TOPSIS

Science.gov (United States)

Masudin, I.; Saputro, T. E.

2016-02-01

In today's technology, electronic trading transaction via internet has been utilized properly with rapid growth. This paper intends to evaluate related to B2C e-commerce website in order to find out the one which meets the usability factors better than another. The influential factors to B2C e-commerce website are determined for two big retailer websites. The factors are investigated based on the consideration of several studies and conformed to the website characteristics. The evaluation is conducted by using different methods namely fuzzy AHP and hierarchical fuzzy TOPSIS so that the final evaluation can be compared. Fuzzy triangular number is adopted to deal with imprecise judgment under fuzzy environment.

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

International Nuclear Information System (INIS)

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H.

2010-01-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

Science.gov (United States)

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Multi-Attribute Decision-Making Based on Bonferroni Mean Operators under Cubic Intuitionistic Fuzzy Set Environment

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Gagandeep Kaur

2018-01-01

Full Text Available Cubic intuitionistic fuzzy (CIF set is the hybrid set which can contain much more information to express an interval-valued intuitionistic fuzzy set and an intuitionistic fuzzy set simultaneously for handling the uncertainties in the data. Unfortunately, there has been no research on the aggregation operators on CIF sets so far. Since an aggregation operator is an important mathematical tool in decision-making problems, the present paper proposes some new Bonferroni mean and weighted Bonferroni mean averaging operators between the cubic intuitionistic fuzzy numbers for aggregating the different preferences of the decision-maker. Then, we develop a decision-making method based on the proposed operators under the cubic intuitionistic fuzzy environment and illustrated with a numerical example. Finally, a comparison analysis between the proposed and the existing approaches have been performed to illustrate the applicability and feasibility of the developed decision-making method.

Introduction to Fuzzy Set Theory

Science.gov (United States)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Approximation Of Multi-Valued Inverse Functions Using Clustering And Sugeno Fuzzy Inference

Science.gov (United States)

Walden, Maria A.; Bikdash, Marwan; Homaifar, Abdollah

1998-01-01

Finding the inverse of a continuous function can be challenging and computationally expensive when the inverse function is multi-valued. Difficulties may be compounded when the function itself is difficult to evaluate. We show that we can use fuzzy-logic approximators such as Sugeno inference systems to compute the inverse on-line. To do so, a fuzzy clustering algorithm can be used in conjunction with a discriminating function to split the function data into branches for the different values of the forward function. These data sets are then fed into a recursive least-squares learning algorithm that finds the proper coefficients of the Sugeno approximators; each Sugeno approximator finds one value of the inverse function. Discussions about the accuracy of the approximation will be included.

Sistem Pendukung Keputusan Pemilihan Line-up Pemain Sepak Bola Menggunakan Metode Fuzzy Multiple Attribute Decision Making dan K-Means Clustering

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Aldi Nurzahputra

2017-07-01

Full Text Available In football, the selection of players line-up is based on their statistical performance. In this research, the line-up selection can implement the decision support system (DSS with FMADM SAW method. The criterias were used are goal, assists, saves, clean sheets, yellow cards, red cards, games, and an own goal. Then, the assessment players performance is using K-Means Clustering. There are two clusters: cluster_cukup and cluster_baik. The system used Manchester City player data in Forward, Mildfilder, Defender and Goal Keeper position. The purpose of this research is applying the FMADM and K-Means Clustering method to the system. Based on the results, the line-up selection can be processed by FMADM method and the performance assessed by K-Means Clustering method. By using the system, the selection and the assessment can be conducted and give the best decision for footbal coach objectively. Dalam sepak bola, pemilihan line-up pemain oleh pelatih dilakukan berdasarkan statistik yang dimiliki pemain. Penelitian ini menerapkan sistem pendukung keputusan (SPK dengan metode FMADM SAW untuk memilih pemain dari hasil pembobotan dari beberapa kriteria, yaitu goal, assist, saves, clean sheet, kartu kuning, kartu merah, main, dan gol bunuh diri. Penilaian performa pemain menggunakan metode K-Means clustering dengan dua cluster, yaitu cluster_cukup dan cluster_baik. Data yang digunakan dalam sistem ini menggunakan data pemain club Manchester City dengan posisi Forward, Mildfilder, Defender, dan Goal Keeper. Berdasarkan hasil yang diteliti, data statistik pemain dapat diolah dengan metode FMADM dan penilaian performa dengan metode K-Means clustering. Dengan adanya sistem ini, pemilihan dan penilaian dilakukan secara objektif dan memberikan pilihan untuk pelatih dalam mengambil keputusan.

A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory.

Science.gov (United States)

Reyna, Valerie F

2012-05-01

Combining meaning, memory, and development, the perennially popular topic of intuition can be approached in a new way. Fuzzy-trace theory integrates these topics by distinguishing between meaning-based gist representations, which support fuzzy (yet advanced) intuition, and superficial verbatim representations of information, which support precise analysis. Here, I review the counterintuitive findings that led to the development of the theory and its most recent extensions to the neuroscience of risky decision making. These findings include memory interference (worse verbatim memory is associated with better reasoning); nonnumerical framing (framing effects increase when numbers are deleted from decision problems); developmental decreases in gray matter and increases in brain connectivity; developmental reversals in memory, judgment, and decision making (heuristics and biases based on gist increase from childhood to adulthood, challenging conceptions of rationality); and selective attention effects that provide critical tests comparing fuzzy-trace theory, expected utility theory, and its variants (e.g., prospect theory). Surprising implications for judgment and decision making in real life are also discussed, notably, that adaptive decision making relies mainly on gist-based intuition in law, medicine, and public health.

Comparıson of Turkey and European Unıon Countrıes’ Health Indıcators by Usıng Fuzzy Clusterıng Analysıs

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Nesrin Alptekin

2014-10-01

Full Text Available In this study, it is aimed to classify of 27 European Union countries and Turkey with the healthcare indicators by using fuzzy clustering analysis. This study also investigates the position of Turkey compared to the European Union countries in terms of healthcare statistics. Fuzzy clustering analysis has been applied to the data obtained from 2012 World Health Report. Based on the Fuzzy clustering analysis, the countries were classified into two different groups. Turkey is placed in the same cluster as Bulgaria, Cyprus, Estonia, Hungary, Latvia, Lithuania, Poland, Romania and Slovakia.

Research and application of fuzzy subtractive clustering model on tensile strength of radiation vulcanization for nitrile-butadiene rubber

International Nuclear Information System (INIS)

Zuo Duwen; Wang Hong; Zhu Nankang

2010-01-01

By use of fuzzy subtractive clustering model, the relationship between tensile strength of radiation vulcanization of NBRL (Nitrile-butadiene rubber latex) and irradiation parameters have been investigated. The correlation coefficient was calculated to be 0.8222 in the comparison of experimental data to the predicted data. It was obvious that fuzzy model identification method is not only high precision with small computation, but also easy to be used. It can directly supply the evolution of tensile strength of NBR by fuzzy modeling method in radiation vulcanization process for nitrile-butadiene rubber. (authors)

Subspace K-means clustering

NARCIS (Netherlands)

Timmerman, Marieke E.; Ceulemans, Eva; De Roover, Kim; Van Leeuwen, Karla

2013-01-01

To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the

Hybrid Clustering And Boundary Value Refinement for Tumor Segmentation using Brain MRI

Science.gov (United States)

Gupta, Anjali; Pahuja, Gunjan

2017-08-01

The method of brain tumor segmentation is the separation of tumor area from Brain Magnetic Resonance (MR) images. There are number of methods already exist for segmentation of brain tumor efficiently. However it’s tedious task to identify the brain tumor from MR images. The segmentation process is extraction of different tumor tissues such as active, tumor, necrosis, and edema from the normal brain tissues such as gray matter (GM), white matter (WM), as well as cerebrospinal fluid (CSF). As per the survey study, most of time the brain tumors are detected easily from brain MR image using region based approach but required level of accuracy, abnormalities classification is not predictable. The segmentation of brain tumor consists of many stages. Manually segmenting the tumor from brain MR images is very time consuming hence there exist many challenges in manual segmentation. In this research paper, our main goal is to present the hybrid clustering which consists of Fuzzy C-Means Clustering (for accurate tumor detection) and level set method(for handling complex shapes) for the detection of exact shape of tumor in minimal computational time. using this approach we observe that for a certain set of images 0.9412 sec of time is taken to detect tumor which is very less in comparison to recent existing algorithm i.e. Hybrid clustering (Fuzzy C-Means and K Means clustering).

Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

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Lingli Jiang

2011-01-01

Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

Profiling Local Optima in K-Means Clustering: Developing a Diagnostic Technique

Science.gov (United States)

Steinley, Douglas

2006-01-01

Using the cluster generation procedure proposed by D. Steinley and R. Henson (2005), the author investigated the performance of K-means clustering under the following scenarios: (a) different probabilities of cluster overlap; (b) different types of cluster overlap; (c) varying samples sizes, clusters, and dimensions; (d) different multivariate…

A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory

Science.gov (United States)

Reyna, Valerie F.

2014-01-01

Combining meaning, memory, and development, the perennially popular topic of intuition can be approached in a new way. Fuzzy-trace theory integrates these topics by distinguishing between meaning-based gist representations, which support fuzzy (yet advanced) intuition, and superficial verbatim representations of information, which support precise analysis. Here, I review the counterintuitive findings that led to the development of the theory and its most recent extensions to the neuroscience of risky decision making. These findings include memory interference (worse verbatim memory is associated with better reasoning); nonnumerical framing (framing effects increase when numbers are deleted from decision problems); developmental decreases in gray matter and increases in brain connectivity; developmental reversals in memory, judgment, and decision making (heuristics and biases based on gist increase from childhood to adulthood, challenging conceptions of rationality); and selective attention effects that provide critical tests comparing fuzzy-trace theory, expected utility theory, and its variants (e.g., prospect theory). Surprising implications for judgment and decision making in real life are also discussed, notably, that adaptive decision making relies mainly on gist-based intuition in law, medicine, and public health. PMID:25530822

A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory

Directory of Open Access Journals (Sweden)

Valerie F. Reyna

2012-05-01

Full Text Available Combining meaning, memory, and development, the perennially popular topic of intuition can be approached in a new way. Fuzzy-trace theory integrates these topics by distinguishing between meaning-based gist representations, which support fuzzy (yet advanced intuition, and superficial verbatim representations of information, which support precise analysis. Here, I review the counterintuitive findings that led to the development of the theory and its most recent extensions to the neuroscience of risky decision making. These findings include memory interference (worse verbatim memory is associated with better reasoning; nonnumerical framing (framing effects increase when numbers are deleted from decision problems; developmental decreases in gray matter and increases in brain connectivity; developmental reversals in memory, judgment, and decision making (heuristics and biases based on gist increase from childhood to adulthood, challenging conceptions of rationality; and selective attention effects that provide critical tests comparing fuzzy-trace theory, expected utility theory, and its variants (e.g., prospect theory. Surprising implications for judgment and decision making in real life are also discussed, notably, that adaptive decision making relies mainly on gist-based intuition in law, medicine, and public health.

Delineation of site-specific management zones by fuzzy clustering of soil and topographic attributes: A case study of East Nile Delta, Egypt

International Nuclear Information System (INIS)

Saleh, A; Belal, A A

2014-01-01

The objective of this study was to define site-specific management zones of 67.2 ha of a wheat pivot field at East of Nile Delta, Egypt for use in precision agriculture based on spatial variability of soil and topographic attributes. The field salinity was analysed by reading the apparent soil electrical conductivity (ECa) with the EM38 sensor horizontally and vertically at 432 locations. The field was sampled for soil attributes systematically with a total of 80 sampling location points. All samples were located using GPS hand held unit. Soil sampling for management zones included soil reaction pH, soil saturation percentage, organic matter, calcium carbonates content, available nitrogen, available phosphorus and available potassium. The field topographic attributes were digital elevation model (DEM), slope, profile curvature, plane curvature, compound topographic index (CTI) and power stream index (PSI). The maps of spatial variability of soil and field topographic attributes were generated using ordinary kriging geostatistical method. Principal component analysis (PCA) was used to determine the most important soil and topographic attributes for representing within-field variability. Principal component analysis of input variables indicated that EM38 horizontal readings (EM38h), soil saturation percentage and digital elevation model were more important attributes for defining field management zones. The fuzzy c-means clustering method was used to divide the field into potential management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Measures of cluster performance indicated no advantage of dividing these fields into more than five management zones. The defined management zones not only provided a better description of the soil properties, but also can direct soil sampling design and provide valuable information for site-specific management in precision agriculture

Constructing APT Attack Scenarios Based on Intrusion Kill Chain and Fuzzy Clustering

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Ru Zhang

2017-01-01

Full Text Available The APT attack on the Internet is becoming more serious, and most of intrusion detection systems can only generate alarms to some steps of APT attack and cannot identify the pattern of the APT attack. To detect APT attack, many researchers established attack models and then correlated IDS logs with the attack models. However, the accuracy of detection deeply relied on the integrity of models. In this paper, we propose a new method to construct APT attack scenarios by mining IDS security logs. These APT attack scenarios can be further used for the APT detection. First, we classify all the attack events by purpose of phase of the intrusion kill chain. Then we add the attack event dimension to fuzzy clustering, correlate IDS alarm logs with fuzzy clustering, and generate the attack sequence set. Next, we delete the bug attack sequences to clean the set. Finally, we use the nonaftereffect property of probability transfer matrix to construct attack scenarios by mining the attack sequence set. Experiments show that the proposed method can construct the APT attack scenarios by mining IDS alarm logs, and the constructed scenarios match the actual situation so that they can be used for APT attack detection.

FCM Clustering Algorithms for Segmentation of Brain MR Images

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Yogita K. Dubey

2016-01-01

Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

K-means Clustering: Lloyd's algorithm

Indian Academy of Sciences (India)

First page Back Continue Last page Overview Graphics. K-means Clustering: Lloyd's algorithm. Refines clusters iteratively. Cluster points using Voronoi partitioning of the centers; Centroids of the clusters determine the new centers. Bad example k = 3, n =4.

Multiple Attribute Group Decision-Making Methods Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Partitioned Bonferroni Mean Aggregation Operators.

Science.gov (United States)

Yin, Kedong; Yang, Benshuo; Li, Xuemei

2018-01-24

In this paper, we investigate multiple attribute group decision making (MAGDM) problems where decision makers represent their evaluation of alternatives by trapezoidal fuzzy two-dimensional uncertain linguistic variable. To begin with, we introduce the definition, properties, expectation, operational laws of trapezoidal fuzzy two-dimensional linguistic information. Then, to improve the accuracy of decision making in some case where there are a sort of interrelationship among the attributes, we analyze partition Bonferroni mean (PBM) operator in trapezoidal fuzzy two-dimensional variable environment and develop two operators: trapezoidal fuzzy two-dimensional linguistic partitioned Bonferroni mean (TF2DLPBM) aggregation operator and trapezoidal fuzzy two-dimensional linguistic weighted partitioned Bonferroni mean (TF2DLWPBM) aggregation operator. Furthermore, we develop a novel method to solve MAGDM problems based on TF2DLWPBM aggregation operator. Finally, a practical example is presented to illustrate the effectiveness of this method and analyses the impact of different parameters on the results of decision-making.

Coexistence of Cluster Structure and Mean-field-type Structure in Medium-weight Nuclei

International Nuclear Information System (INIS)

Taniguchi, Yasutaka; Horiuchi, Hisashi; Kimura, Masaaki

2006-01-01

We have studied the coexistence of cluster structure and mean-field-type structure in 20Ne and 40Ca using Antisymmetrized Molecular Dynamics (AMD) + Generator Coordinate Method (GCM). By energy variation with new constraint for clustering, we calculate cluster structure wave function. Superposing cluster structure wave functions and mean-field-type structure wave function, we found that 8Be-12C, α-36Ar and 12C-28Si cluster structure are important components of K π = 0 3 + band of 20Ne, that of normal deformed band of 40Ca and that of super deformed band of 40Ca, respectively

Decision Making in Uncertain Rural Scenarios by means of Fuzzy TOPSIS Method

Directory of Open Access Journals (Sweden)

Eva Armero

2011-01-01

Full Text Available A great deal of uncertain information which is difficult to quantify is taken into account by farmers and experts in the enterprise when making decisions. We are interested in the problems of the implementation of a rabbit-breeding farm. One of the first decisions to be taken refers to the design or type of structure for housing the animals, which is determined by the level of environmental control sought to be maintained in its interior. A farmer was consulted, and his answers were incorporated into the analysis, by means of the fuzzy TOPSIS methodology. The main purpose of this paper is to study the problem by means of the fuzzy TOPSIS method as multicriteria decision making, when the information was given in linguistic terms.

Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering.

Science.gov (United States)

Meng, Lei; Tan, Ah-Hwee; Wunsch, Donald C

2016-12-01

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm

Directory of Open Access Journals (Sweden)

Yan Hong Chen

2016-01-01

Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.

Sadhana | Indian Academy of Sciences

Indian Academy of Sciences (India)

2018-03-16

Mar 16, 2018 ... Fuzzy C Means clustering, one of the predominant segmentation algorithms, requires prior knowledge of number of clusters in the image and is sensitive to noise and outliers. Determining the number of clusters and including spatial information to basic Fuzzy C Means clustering are done in numerous ways ...

Fuzzy rule-based forecast of meteorological drought in western Niger

Science.gov (United States)

Abdourahamane, Zakari Seybou; Acar, Reşat

2018-01-01

Understanding the causes of rainfall anomalies in the West African Sahel to effectively predict drought events remains a challenge. The physical mechanisms that influence precipitation in this region are complex, uncertain, and imprecise in nature. Fuzzy logic techniques are renowned to be highly efficient in modeling such dynamics. This paper attempts to forecast meteorological drought in Western Niger using fuzzy rule-based modeling techniques. The 3-month scale standardized precipitation index (SPI-3) of four rainfall stations was used as predictand. Monthly data of southern oscillation index (SOI), South Atlantic sea surface temperature (SST), relative humidity (RH), and Atlantic sea level pressure (SLP), sourced from the National Oceanic and Atmosphere Administration (NOAA), were used as predictors. Fuzzy rules and membership functions were generated using fuzzy c-means clustering approach, expert decision, and literature review. For a minimum lead time of 1 month, the model has a coefficient of determination R 2 between 0.80 and 0.88, mean square error (MSE) below 0.17, and Nash-Sutcliffe efficiency (NSE) ranging between 0.79 and 0.87. The empirical frequency distributions of the predicted and the observed drought classes are equal at the 99% of confidence level based on two-sample t test. Results also revealed the discrepancy in the influence of SOI and SLP on drought occurrence at the four stations while the effect of SST and RH are space independent, being both significantly correlated (at α based forecast model shows better forecast skills.

Land cover classification using reformed fuzzy C-means

Indian Academy of Sciences (India)

This paper uses segmentation based on unsupervised clustering techniques for classification of land cover. ∗ ... and unsupervised classification can be solved by FCM. ..... They also act as input to the development and monitoring of a range of ...

Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

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Ya’nan Wang

2016-01-01

Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.

Science.gov (United States)

Almaraashi, Majid

2017-01-01

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.

Classification of protein profiles using fuzzy clustering techniques

DEFF Research Database (Denmark)

Karemore, Gopal; Mullick, Jhinuk B.; Sujatha, R.

2010-01-01

 Present  study  has  brought  out  a  comparison  of PCA  and  fuzzy  clustering  techniques  in  classifying  protein profiles  (chromatogram)  of  homogenates  of  different  tissue origins:  Ovarian,  Cervix,  Oral  cancers,  which  were  acquired using HPLC–LIF (High Performance Liquid...... Chromatography- Laser   Induced   Fluorescence)   method   developed   in   our laboratory. Study includes 11 chromatogram spectra each from oral,  cervical,  ovarian  cancers  as  well  as  healthy  volunteers. Generally  multivariate  analysis  like  PCA  demands  clear  data that   is   devoid   of   day......   PCA   mapping   in   classifying   various cancers from healthy spectra with classification rate up to 95 % from  60%.  Methods  are  validated  using  various  clustering indexes   and   shows   promising   improvement   in   developing optical pathology like HPLC-LIF for early detection of various...

Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset

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Amir Ahmad

2016-01-01

Full Text Available The early diagnosis of breast cancer is an important step in a fight against the disease. Machine learning techniques have shown promise in improving our understanding of the disease. As medical datasets consist of data points which cannot be precisely assigned to a class, fuzzy methods have been useful for studying of these datasets. Sometimes breast cancer datasets are described by categorical features. Many fuzzy clustering algorithms have been developed for categorical datasets. However, in most of these methods Hamming distance is used to define the distance between the two categorical feature values. In this paper, we use a probabilistic distance measure for the distance computation among a pair of categorical feature values. Experiments demonstrate that the distance measure performs better than Hamming distance for Wisconsin breast cancer data.

Cluster radioactive decay within the preformed cluster model using relativistic mean-field theory densities

International Nuclear Information System (INIS)

Singh, BirBikram; Patra, S. K.; Gupta, Raj K.

2010-01-01

We have studied the (ground-state) cluster radioactive decays within the preformed cluster model (PCM) of Gupta and collaborators [R. K. Gupta, in Proceedings of the 5th International Conference on Nuclear Reaction Mechanisms, Varenna, edited by E. Gadioli (Ricerca Scientifica ed Educazione Permanente, Milano, 1988), p. 416; S. S. Malik and R. K. Gupta, Phys. Rev. C 39, 1992 (1989)]. The relativistic mean-field (RMF) theory is used to obtain the nuclear matter densities for the double folding procedure used to construct the cluster-daughter potential with M3Y nucleon-nucleon interaction including exchange effects. Following the PCM approach, we have deduced empirically the preformation probability P 0 emp from the experimental data on both the α- and exotic cluster-decays, specifically of parents in the trans-lead region having doubly magic 208 Pb or its neighboring nuclei as daughters. Interestingly, the RMF-densities-based nuclear potential supports the concept of preformation for both the α and heavier clusters in radioactive nuclei. P 0 α(emp) for α decays is almost constant (∼10 -2 -10 -3 ) for all the parent nuclei considered here, and P 0 c(emp) for cluster decays of the same parents decrease with the size of clusters emitted from different parents. The results obtained for P 0 c(emp) are reasonable and are within two to three orders of magnitude of the well-accepted phenomenological model of Blendowske-Walliser for light clusters.

A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

OpenAIRE

Li, Guohui; Zhang, Songling; Yang, Hong

2017-01-01

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

Fuzzy cluster quantitative computations of component mass transfer in rocks or minerals

International Nuclear Information System (INIS)

Liu Dezheng

2000-01-01

The author advances a new component mass transfer quantitative computation method on the basis of closure nature of mass percentage of components in rocks or minerals. Using fuzzy dynamic cluster analysis, and calculating restore closure difference, and determining type of difference, and assisted by relevant diagnostic parameters, the method gradually screens out the true constant component. Then, true mass percentage and mass transfer quantity of components of metabolic rocks or minerals are calculated by applying the true constant component fixed coefficient. This method is called true constant component fixed method (TCF method)

Clustering of financial time series

Science.gov (United States)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

Hesitant triangular fuzzy information aggregation operators based on Bonferroni means and their application to multiple attribute decision making.

Science.gov (United States)

Wang, Chunyong; Li, Qingguo; Zhou, Xiaoqiang; Yang, Tian

2014-01-01

We investigate the multiple attribute decision-making (MADM) problems with hesitant triangular fuzzy information. Firstly, definition and some operational laws of hesitant triangular fuzzy elements are introduced. Then, we develop some hesitant triangular fuzzy aggregation operators based on Bonferroni means and discuss their basic properties. Some existing operators can be viewed as their special cases. Next, we apply the proposed operators to deal with multiple attribute decision-making problems under hesitant triangular fuzzy environment. Finally, an illustrative example is given to show the developed method and demonstrate its practicality and effectiveness.

The C4 clustering algorithm: Clusters of galaxies in the Sloan Digital Sky Survey

Energy Technology Data Exchange (ETDEWEB)

Miller, Christopher J.; Nichol, Robert; Reichart, Dan; Wechsler, Risa H.; Evrard, August; Annis, James; McKay, Timothy; Bahcall, Neta; Bernardi, Mariangela; Boehringer,; Connolly, Andrew; Goto, Tomo; Kniazev, Alexie; Lamb, Donald; Postman, Marc; Schneider, Donald; Sheth, Ravi; Voges, Wolfgang; /Cerro-Tololo InterAmerican Obs. /Portsmouth U.,

2005-03-01

We present the ''C4 Cluster Catalog'', a new sample of 748 clusters of galaxies identified in the spectroscopic sample of the Second Data Release (DR2) of the Sloan Digital Sky Survey (SDSS). The C4 cluster-finding algorithm identifies clusters as overdensities in a seven-dimensional position and color space, thus minimizing projection effects that have plagued previous optical cluster selection. The present C4 catalog covers {approx}2600 square degrees of sky and ranges in redshift from z = 0.02 to z = 0.17. The mean cluster membership is 36 galaxies (with redshifts) brighter than r = 17.7, but the catalog includes a range of systems, from groups containing 10 members to massive clusters with over 200 cluster members with redshifts. The catalog provides a large number of measured cluster properties including sky location, mean redshift, galaxy membership, summed r-band optical luminosity (L{sub r}), velocity dispersion, as well as quantitative measures of substructure and the surrounding large-scale environment. We use new, multi-color mock SDSS galaxy catalogs, empirically constructed from the {Lambda}CDM Hubble Volume (HV) Sky Survey output, to investigate the sensitivity of the C4 catalog to the various algorithm parameters (detection threshold, choice of passbands and search aperture), as well as to quantify the purity and completeness of the C4 cluster catalog. These mock catalogs indicate that the C4 catalog is {approx_equal}90% complete and 95% pure above M{sub 200} = 1 x 10{sup 14} h{sup -1}M{sub {circle_dot}} and within 0.03 {le} z {le} 0.12. Using the SDSS DR2 data, we show that the C4 algorithm finds 98% of X-ray identified clusters and 90% of Abell clusters within 0.03 {le} z {le} 0.12. Using the mock galaxy catalogs and the full HV dark matter simulations, we show that the L{sub r} of a cluster is a more robust estimator of the halo mass (M{sub 200}) than the galaxy line-of-sight velocity dispersion or the richness of the cluster

An Ontological-Fuzzy Approach to Advance Reservation in Multi-Cluster Grids

International Nuclear Information System (INIS)

Ferreira, D J; Dantas, M A R; Bauer, Michael A

2010-01-01

Advance reservation is an important mechanism for a successful utilization of available resources in distributed multi-cluster environments. This mechanism allows, for example, a user to provide parameters aiming to satisfy requirements related to applications' execution time and temporal dependence. This predictability can lead the system to reach higher levels of QoS. However, the support for advance reservation has been restricted due to the complexity of large scale configurations and also dynamic changes verified in these systems. In this research work it is proposed an advance reservation method, based on a ontology-fuzzy approach. It allows a user to reserve a wide variety of resources and enable large jobs to be reserved among different nodes. In addition, it dynamically verifies the possibility of reservation with the local RMS, avoiding future allocation conflicts. Experimental results of the proposal, through simulation, indicate that the proposed mechanism reached a successful level of flexibility for large jobs and more appropriated distribution of resources in a distributed multi-cluster configuration.

An Ontological-Fuzzy Approach to Advance Reservation in Multi-Cluster Grids

Energy Technology Data Exchange (ETDEWEB)

Ferreira, D J; Dantas, M A R; Bauer, Michael A, E-mail: ded@inf.ufsc.br, E-mail: mario@inf.ufsc.br, E-mail: bauer@csd.uwo.ca

2010-11-01

Advance reservation is an important mechanism for a successful utilization of available resources in distributed multi-cluster environments. This mechanism allows, for example, a user to provide parameters aiming to satisfy requirements related to applications' execution time and temporal dependence. This predictability can lead the system to reach higher levels of QoS. However, the support for advance reservation has been restricted due to the complexity of large scale configurations and also dynamic changes verified in these systems. In this research work it is proposed an advance reservation method, based on a ontology-fuzzy approach. It allows a user to reserve a wide variety of resources and enable large jobs to be reserved among different nodes. In addition, it dynamically verifies the possibility of reservation with the local RMS, avoiding future allocation conflicts. Experimental results of the proposal, through simulation, indicate that the proposed mechanism reached a successful level of flexibility for large jobs and more appropriated distribution of resources in a distributed multi-cluster configuration.

A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation

Directory of Open Access Journals (Sweden)

Błaszczuk Artur

2017-03-01

Full Text Available The interrelation between fuzzy logic and cluster renewal approaches for heat transfer modeling in a circulating fluidized bed (CFB has been established based on a local furnace data. The furnace data have been measured in a 1296 t/h CFB boiler with low level of flue gas recirculation. In the present study, the bed temperature and suspension density were treated as experimental variables along the furnace height. The measured bed temperature and suspension density were varied in the range of 1131-1156 K and 1.93-6.32 kg/m3, respectively. Using the heat transfer coefficient for commercial CFB combustor, two empirical heat transfer correlation were developed in terms of important operating parameters including bed temperature and also suspension density. The fuzzy logic results were found to be in good agreement with the corresponding experimental heat transfer data obtained based on cluster renewal approach. The predicted bed-to-wall heat transfer coefficient covered a range of 109-241 W/(m2K and 111-240 W/(m2K, for fuzzy logic and cluster renewal approach respectively. The divergence in calculated heat flux recovery along the furnace height between fuzzy logic and cluster renewal approach did not exceeded ±2%.

Choosing the Number of Clusters in K-Means Clustering

Science.gov (United States)

Steinley, Douglas; Brusco, Michael J.

2011-01-01

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple…

Ensemble Genetic Fuzzy Neuro Model Applied for the Emergency Medical Service via Unbalanced Data Evaluation

Directory of Open Access Journals (Sweden)

Muammar Sadrawi

2018-03-01

Full Text Available Equally partitioned data are essential for prediction. However, in some important cases, the data distribution is severely unbalanced. In this study, several algorithms are utilized to maximize the learning accuracy when dealing with a highly unbalanced dataset. A linguistic algorithm is applied to evaluate the input and output relationship, namely Fuzzy c-Means (FCM, which is applied as a clustering algorithm for the majority class to balance the minority class data from about 3 million cases. Each cluster is used to train several artificial neural network (ANN models. Different techniques are applied to generate an ensemble genetic fuzzy neuro model (EGFNM in order to select the models. The first ensemble technique, the intra-cluster EGFNM, works by evaluating the best combination from all the models generated by each cluster. Another ensemble technique is the inter-cluster model EGFNM, which is based on selecting the best model from each cluster. The accuracy of these techniques is evaluated using the receiver operating characteristic (ROC via its area under the curve (AUC. Results show that the AUC of the unbalanced data is 0.67974. The random cluster and best ANN single model have AUCs of 0.7177 and 0.72806, respectively. For the ensemble evaluations, the intra-cluster and the inter-cluster EGFNMs produce 0.7293 and 0.73038, respectively. In conclusion, this study achieved improved results by performing the EGFNM method compared with the unbalanced training. This study concludes that selecting several best models will produce a better result compared with all models combined.

Fuzzy cluster analysis on trace elements of Hangzhou Jiaotan Guan Porcelain

International Nuclear Information System (INIS)

Gao Zhengyao; Liu Youe; Chen Songhua

1997-01-01

Forty samples of South Song 'Jiaotan Guankiln' are analyzed by neutron activation analysis (NAA). The 36 trace element contents in every sample are determined. This trace elements are analyzed by fuzzy cluster method. The result shows that the source of glaze raw material of South Song Guan porcelain is clearly different from that of the body raw material. For Guan kiln of South Song dynasty there was a very stable and lasting source of raw material of glaze and body. The archaeological problems are clarified. The glaze material and body material of modern Guan porcelain are different from those of the ancient Guan Porcelain

Applying Fuzzy Possibilistic Methods on Critical Objects

DEFF Research Database (Denmark)

Yazdani, Hossein; Ortiz-Arroyo, Daniel; Choros, Kazimierz

2016-01-01

Providing a flexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects is evaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect...... the performance of the clustering algorithms if they remain in a specific cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning...... methods to provide a proper searching space for data objects. The membership functions used by each method when dealing with critical objects is also evaluated. Our results show that relaxing the conditions of participation for data objects in as many partitions as they can, is beneficial....

A Variational Level Set Model Combined with FCMS for Image Clustering Segmentation

Directory of Open Access Journals (Sweden)

Liming Tang

2014-01-01

Full Text Available The fuzzy C means clustering algorithm with spatial constraint (FCMS is effective for image segmentation. However, it lacks essential smoothing constraints to the cluster boundaries and enough robustness to the noise. Samson et al. proposed a variational level set model for image clustering segmentation, which can get the smooth cluster boundaries and closed cluster regions due to the use of level set scheme. However it is very sensitive to the noise since it is actually a hard C means clustering model. In this paper, based on Samson’s work, we propose a new variational level set model combined with FCMS for image clustering segmentation. Compared with FCMS clustering, the proposed model can get smooth cluster boundaries and closed cluster regions due to the use of level set scheme. In addition, a block-based energy is incorporated into the energy functional, which enables the proposed model to be more robust to the noise than FCMS clustering and Samson’s model. Some experiments on the synthetic and real images are performed to assess the performance of the proposed model. Compared with some classical image segmentation models, the proposed model has a better performance for the images contaminated by different noise levels.

Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering

Institute of Scientific and Technical Information of China (English)

FENG Yu-hu

2005-01-01

By constructing a mean-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts: a real-valued non-random recurrence equation and the standard Kalman filtering.

Implementasi KD-Tree K-Means Clustering untuk Klasterisasi Dokumen

Directory of Open Access Journals (Sweden)

Eric Budiman Gosno

2013-09-01

Full Text Available Klasterisasi dokumen adalah suatu proses pengelompokan dokumen secara otomatis dan unsupervised. Klasterisasi dokumen merupakan permasalahan yang sering ditemui dalam berbagai bidang seperti text mining dan sistem temu kembali informasi. Metode klasterisasi dokumen yang memiliki akurasi dan efisiensi waktu yang tinggi sangat diperlukan untuk meningkatkan hasil pada mesin pencari web,  dan untuk proses filtering. Salah satu metode klasterisasi yang telah dikenal dan diaplikasikan dalam klasterisasi dokumen adalah K-Means Clustering. Tetapi K-Means Clustering sensitif terhadap pemilihan posisi awal dari titik tengah klaster sehingga pemilihan posisi awal dari titik tengah klaster yang buruk akan mengakibatkan K-Means Clustering terjebak dalam local optimum. KD-Tree K-Means Clustering merupakan perbaikan dari K-Means Clustering. KD-Tree K-Means Clustering menggunakan struktur data K-Dimensional Tree dan nilai kerapatan pada proses inisialisasi titik tengah klaster. Pada makalah ini diimplementasikan algoritma KD-Tree K-Means Clustering untuk permasalahan klasterisasi dokumen. Performa klasterisasi dokumen yang dihasilkan oleh metode KD-Tree K-Means Clustering pada data set 20 newsgroup memiliki nilai distorsi 3×105 lebih rendah dibandingkan dengan nilai rerata distorsi K-Means Clustering dan nilai NIG 0,09 lebih baik dibandingkan dengan nilai NIG K-Means Clustering.

Neutrosophic Hierarchical Clustering Algoritms

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Rıdvan Şahin

2014-03-01

Full Text Available Interval neutrosophic set (INS is a generalization of interval valued intuitionistic fuzzy set (IVIFS, whose the membership and non-membership values of elements consist of fuzzy range, while single valued neutrosophic set (SVNS is regarded as extension of intuitionistic fuzzy set (IFS. In this paper, we extend the hierarchical clustering techniques proposed for IFSs and IVIFSs to SVNSs and INSs respectively. Based on the traditional hierarchical clustering procedure, the single valued neutrosophic aggregation operator, and the basic distance measures between SVNSs, we define a single valued neutrosophic hierarchical clustering algorithm for clustering SVNSs. Then we extend the algorithm to classify an interval neutrosophic data. Finally, we present some numerical examples in order to show the effectiveness and availability of the developed clustering algorithms.

Prediction of line failure fault based on weighted fuzzy dynamic clustering and improved relational analysis

Science.gov (United States)

Meng, Xiaocheng; Che, Renfei; Gao, Shi; He, Juntao

2018-04-01

With the advent of large data age, power system research has entered a new stage. At present, the main application of large data in the power system is the early warning analysis of the power equipment, that is, by collecting the relevant historical fault data information, the system security is improved by predicting the early warning and failure rate of different kinds of equipment under certain relational factors. In this paper, a method of line failure rate warning is proposed. Firstly, fuzzy dynamic clustering is carried out based on the collected historical information. Considering the imbalance between the attributes, the coefficient of variation is given to the corresponding weights. And then use the weighted fuzzy clustering to deal with the data more effectively. Then, by analyzing the basic idea and basic properties of the relational analysis model theory, the gray relational model is improved by combining the slope and the Deng model. And the incremental composition and composition of the two sequences are also considered to the gray relational model to obtain the gray relational degree between the various samples. The failure rate is predicted according to the principle of weighting. Finally, the concrete process is expounded by an example, and the validity and superiority of the proposed method are verified.

*K-means and cluster models for cancer signatures.

Science.gov (United States)

Kakushadze, Zura; Yu, Willie

2017-09-01

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.

A fuzzy automated object classification by infrared laser camera

Science.gov (United States)

Kanazawa, Seigo; Taniguchi, Kazuhiko; Asari, Kazunari; Kuramoto, Kei; Kobashi, Syoji; Hata, Yutaka

2011-06-01

Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.

Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

Science.gov (United States)

Rustam, Z.; Talita, A. S.

2017-07-01

Intrusion Detection System (IDS) is an essential part of security systems to strengthen the security of information systems. IDS can be used to detect the abuse by intruders who try to get into the network system in order to access and utilize the available data sources in the system. There are two approaches of IDS, Misuse Detection and Anomaly Detection (behavior-based intrusion detection). Fuzzy clustering-based methods have been widely used to solve Anomaly Detection problems. Other than using fuzzy membership concept to determine the object to a cluster, other approaches as in combining fuzzy and possibilistic membership or feature-weighted based methods are also used. We propose Fuzzy Kernel k-Medoids that combining fuzzy and possibilistic membership as a powerful method to solve anomaly detection problem since on numerical experiment it is able to classify IDS benchmark data into five different classes simultaneously. We classify IDS benchmark data KDDCup'99 data set into five different classes simultaneously with the best performance was achieved by using 30 % of training data with clustering accuracy reached 90.28 percent.

The Anonymization Protection Algorithm Based on Fuzzy Clustering for the Ego of Data in the Internet of Things

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Mingshan Xie

2017-01-01

Full Text Available In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange.

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

In unsupervised classification, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...

Implementation of K-Means Clustering Method for Electronic Learning Model

Science.gov (United States)

Latipa Sari, Herlina; Suranti Mrs., Dewi; Natalia Zulita, Leni

2017-12-01

Teaching and Learning process at SMK Negeri 2 Bengkulu Tengah has applied e-learning system for teachers and students. The e-learning was based on the classification of normative, productive, and adaptive subjects. SMK Negeri 2 Bengkulu Tengah consisted of 394 students and 60 teachers with 16 subjects. The record of e-learning database was used in this research to observe students’ activity pattern in attending class. K-Means algorithm in this research was used to classify students’ learning activities using e-learning, so that it was obtained cluster of students’ activity and improvement of student’s ability. Implementation of K-Means Clustering method for electronic learning model at SMK Negeri 2 Bengkulu Tengah was conducted by observing 10 students’ activities, namely participation of students in the classroom, submit assignment, view assignment, add discussion, view discussion, add comment, download course materials, view article, view test, and submit test. In the e-learning model, the testing was conducted toward 10 students that yielded 2 clusters of membership data (C1 and C2). Cluster 1: with membership percentage of 70% and it consisted of 6 members, namely 1112438 Anggi Julian, 1112439 Anis Maulita, 1112441 Ardi Febriansyah, 1112452 Berlian Sinurat, 1112460 Dewi Anugrah Anwar and 1112467 Eka Tri Oktavia Sari. Cluster 2:with membership percentage of 30% and it consisted of 4 members, namely 1112463 Dosita Afriyani, 1112471 Erda Novita, 1112474 Eskardi and 1112477 Fachrur Rozi.

Application of Interval Type-2 Fuzzy Logic to polypropylene business policy in a petrochemical plant in India

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Dipak Kumar Jana

2018-01-01

Full Text Available This paper presents a new approach to predict the quality of polypropylene in petrochemical plants. The proposed approach constructs four different models, based on a large number of data collected from a renowned petrochemical plant in India and uses it to predict the polypropylene quality. The quality of polypropylene depends on the indices such as melt flow index and the xylene solubility of the product and the parameters controlling these two indices are hydrogen flow, donor flow, pressure and temperature of polymerization reactors. Mamdani Interval Type-2 Fuzzy Logic inference systems are formed for first time. The model outcomes are compared with the collected plant data and a sequence of sensitivity analysis elects the most suitable model from them. Some sensitivity analyses are provided using Fuzzy C-Means Clustering to the models.

K-means clustering versus validation measures: a data-distribution perspective.

Science.gov (United States)

Xiong, Hui; Wu, Junjie; Chen, Jian

2009-04-01

K-means is a well-known and widely used partitional clustering method. While there are considerable research efforts to characterize the key features of the K-means clustering algorithm, further investigation is needed to understand how data distributions can have impact on the performance of K-means clustering. To that end, in this paper, we provide a formal and organized study of the effect of skewed data distributions on K-means clustering. Along this line, we first formally illustrate that K-means tends to produce clusters of relatively uniform size, even if input data have varied "true" cluster sizes. In addition, we show that some clustering validation measures, such as the entropy measure, may not capture this uniform effect and provide misleading information on the clustering performance. Viewed in this light, we provide the coefficient of variation (CV) as a necessary criterion to validate the clustering results. Our findings reveal that K-means tends to produce clusters in which the variations of cluster sizes, as measured by CV, are in a range of about 0.3-1.0. Specifically, for data sets with large variation in "true" cluster sizes (e.g., CV > 1.0), K-means reduces variation in resultant cluster sizes to less than 1.0. In contrast, for data sets with small variation in "true" cluster sizes (e.g., CV K-means increases variation in resultant cluster sizes to greater than 0.3. In other words, for the earlier two cases, K-means produces the clustering results which are away from the "true" cluster distributions.

FUZZY CLUSTERING: APPLICATION ON ORGANIZATIONAL METAPHORS IN BRAZILIAN COMPANIES

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Angel Cobo

2012-08-01

Full Text Available Different theories of organization and management are based on implicit images or metaphors. Nevertheless, a quantitative approach is needed to minimize human subjectivity or bias on metaphors studies. Hence, this paper analyzed the presence of metaphors and clustered them using fuzzy data mining techniques in a sample of 61 Brazilian companies that operate in the state of Rio Grande do Sul. For this purpose the results of a questionnaire answered by 198 employees of companies in the sample were analyzed by R free software. The results show that it is difficult to find a clear image in most organizations. In most cases characteristics of different images or metaphors are observed, so soft computing techniques are particularly appropriate for this type of analysis. However, according to these results, it is noted that the most present image in the organizations studied is that of “organisms” and the least present image is that of a “political system” and of an “instrument of domination”

Supply chain management under fuzziness recent developments and techniques

CERN Document Server

Öztayşi, Başar

2014-01-01

Supply Chain Management Under Fuzziness presents recently developed fuzzy models and techniques for supply chain management. These include: fuzzy PROMETHEE, fuzzy AHP, fuzzy ANP, fuzzy VIKOR, fuzzy DEMATEL, fuzzy clustering, fuzzy linear programming, and fuzzy inference systems. The book covers both practical applications and new developments concerning these methods. This book offers an excellent resource for researchers and practitioners in supply chain management and logistics, and will provide them with new suggestions and directions for future research. Moreover, it will support graduate students in their university courses, such as specialized courses on supply chains and logistics, as well as related courses in the fields of industrial engineering, engineering management and business administration.

Performance Evaluation of Incremental K-means Clustering Algorithm

OpenAIRE

Chakraborty, Sanjay; Nagwani, N. K.

2014-01-01

The incremental K-means clustering algorithm has already been proposed and analysed in paper [Chakraborty and Nagwani, 2011]. It is a very innovative approach which is applicable in periodically incremental environment and dealing with a bulk of updates. In this paper the performance evaluation is done for this incremental K-means clustering algorithm using air pollution database. This paper also describes the comparison on the performance evaluations between existing K-means clustering and i...

Stability analysis of polynomial fuzzy models via polynomial fuzzy Lyapunov functions

OpenAIRE

Bernal Reza, Miguel Ángel; Sala, Antonio; JAADARI, ABDELHAFIDH; Guerra, Thierry-Marie

2011-01-01

In this paper, the stability of continuous-time polynomial fuzzy models by means of a polynomial generalization of fuzzy Lyapunov functions is studied. Fuzzy Lyapunov functions have been fruitfully used in the literature for local analysis of Takagi-Sugeno models, a particular class of the polynomial fuzzy ones. Based on a recent Taylor-series approach which allows a polynomial fuzzy model to exactly represent a nonlinear model in a compact set of the state space, it is shown that a refinemen...

Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

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M. Amiri

2012-10-01

Full Text Available Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on Nasdaq from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods.

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.

Study on internal to surface fingerprint correlation using optical coherence tomography and internal fingerprint extraction

CSIR Research Space (South Africa)

Darlow, LN

2015-11-01

Full Text Available of, the above-mentioned works. Internal fingerprint zone detection is an improvement as it uses fuzzy c-means to improve clustering performance, uses better cluster result postprocessing, and improves upon the fine-tuning procedure through... Internal fingerprint extraction consists of two main parts: fingerprint zone detection and extraction. Zone detection uses fuzzy c-means clustering to approximate the location of the papillary junction (i.e., the internal fingerprint zone). Edge detection...

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

Science.gov (United States)

Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.

2018-04-01

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

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Ashlock Daniel

2009-08-01

Full Text Available Abstract Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering.

Science.gov (United States)

Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

2009-08-22

Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.

Clustering of resting state networks.

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Megan H Lee

Full Text Available The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

Science.gov (United States)

Tsaur, Ruey-Chyn

2015-02-01

In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images

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M. Anitha

2012-04-01

Full Text Available Settings White Matter lesions (WMLs are small areas of dead cells found in parts of the brain. In general, it is difficult for medical experts to accurately quantify the WMLs due to decreased contrast between White Matter (WM and Grey Matter (GM. The aim of this paper is to
automatically detect the White Matter Lesions which is present in the brains of elderly people. WML detection process includes the following stages: 1. Image preprocessing, 2. Clustering (Fuzzy c-means clustering, Geostatistical Possibilistic clustering and Geostatistical Fuzzy clustering and 3.Optimization using Particle Swarm Optimization (PSO. The proposed system is tested on a database of 208 MRI images. GFCM yields high sensitivity of 89%, specificity of 94% and overall accuracy of 93% over FCM and GPC. The clustered brain images are then subjected to Particle Swarm Optimization (PSO. The optimized result obtained from GFCM-PSO provides sensitivity of 90%, specificity of 94% and accuracy of 95%. The detection results reveals that GFCM and GFCMPSO better localizes the large regions of lesions and gives less false positive rate when compared to GPC and GPC-PSO which captures the largest loads of WMLs only in the upper ventral horns of the brain.

Android Malware Classification Using K-Means Clustering Algorithm

Science.gov (United States)

Hamid, Isredza Rahmi A.; Syafiqah Khalid, Nur; Azma Abdullah, Nurul; Rahman, Nurul Hidayah Ab; Chai Wen, Chuah

2017-08-01

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Compound Option Pricing under Fuzzy Environment

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Xiandong Wang

2014-01-01

Full Text Available Considering the uncertainty of a financial market includes two aspects: risk and vagueness; in this paper, fuzzy sets theory is applied to model the imprecise input parameters (interest rate and volatility. We present the fuzzy price of compound option by fuzzing the interest and volatility in Geske’s compound option pricing formula. For each α, the α-level set of fuzzy prices is obtained according to the fuzzy arithmetics and the definition of fuzzy-valued function. We apply a defuzzification method based on crisp possibilistic mean values of the fuzzy interest rate and fuzzy volatility to obtain the crisp possibilistic mean value of compound option price. Finally, we present a numerical analysis to illustrate the compound option pricing under fuzzy environment.

Environmental impact assessment by means of a procedure based on fuzzy logic: A practical application

International Nuclear Information System (INIS)

Peche, Roberto; Rodriguez, Esther

2011-01-01

This study shows the practical application of the EIA method based on fuzzy logic proposed by the authors (Peche and Rodriguez, 2009) to a simplified case of study-the activity of a petrol station throughout its exploitation. The intensity (p 1 ), the extent (p 2 ) and the persistence (p 3 ) were the properties selected to describe the impacts and their respective assessment functions v-bar i =f(p-bar i ) were determined. The main actions (A) and potentially affected environmental factors (F) were selected. Every impact was identified by a pair A-F and the values of the three impact properties were estimated for each of them by means of triangular fuzzy numbers. Subsequently, the fuzzy estimation of every impact was carried out, the estimation of the impact A 1 -F 2 (V-bar 1 ) being explained in detail. Every impact was simultaneously represented by its corresponding generalised confidence interval and membership function. Since the membership functions of all impacts were similar to triangular fuzzy numbers, a triangular approach (TA) was used to describe every impact. A triangular approach coefficient (TAC) was introduced to quantify the similarity of each fuzzy number and its corresponding triangular approach, where TAC (V-bar ) element of (0, 1] and TAC being 1 when the fuzzy number is triangular. The TACs-ranging from 0.96 to 0.99-proved that TAs were valid in all cases. Next, the total positive and negative impacts-TV-bar + and TV-bar - were calculated and later, the fuzzy value of the total environmental impact TV-bar was determined from them. Finally, the defuzzification of TV-bar led to the punctual impact estimator TV (1) = -88.50 and its corresponding uncertainty interval [δ l (TV-bar),δ r (TV-bar)]=[6.52,6.96], which represent the total value of the EI. In conclusion, the EIA method enabled the integration of heterogeneous impacts, which exerted influence on environmental factors of a very diverse nature in very different ways, into a global impact

Intuitionistic Trapezoidal Fuzzy Group Decision-Making Based on Prospect Choquet Integral Operator and Grey Projection Pursuit Dynamic Cluster

Directory of Open Access Journals (Sweden)

Jiahang Yuan

2017-01-01

Full Text Available In consideration of the interaction among attributes and the influence of decision makers’ risk attitude, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. With respect to a multiattribute group decision-making problem, the prospect value functions of intuitionistic trapezoidal fuzzy numbers are aggregated by the proposed operator; then a grey relation-projection pursuit dynamic cluster method is developed to obtain the ranking of alternatives; the firefly algorithm is used to optimize the objective function of projection for obtaining the best projection direction of grey correlation projection values, and the grey correlation projection values are evaluated, which are applied to classify, rank, and prefer the alternatives. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.

A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

International Nuclear Information System (INIS)

Nguyen, Sy Dzung; Choi, Seung-Bok

2012-01-01

This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input–output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results. (paper)

A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers

Science.gov (United States)

Dzung Nguyen, Sy; Choi, Seung-Bok

2012-08-01

This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input-output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results.

Genetic algorithm with fuzzy clustering for optimization of nuclear reactor problems

International Nuclear Information System (INIS)

Machado, Marcelo Dornellas; Sacco, Wagner Figueiredo; Schirru, Roberto

2000-01-01

Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to introduce a new niching technique based on the fuzzy clustering method FCM, bearing in mind its eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. tests are performed using widely known test functions and their results show that the new method is quite promising, specially to a future application in real world problems like the nuclear reactor core reload. (author)

ANALISIS CLUSTER K-MEANS DALAM PENGELOMPOKAN KEMAMPUAN MAHASISWA

Directory of Open Access Journals (Sweden)

B. Poerwanto

2016-12-01

Full Text Available Abstract. Cluster Analysis, K-Means Algorithm, Student Classification. This study aims to classify students based on learning outcomes for subject the basic of statistics (DDS, which is measured based on attendance, task, midterm (UTS, and final exams (UAS to further used to evaluate learning for subjects that require analysis of quantitative . This study uses k-means cluster analysis to classify the students into three groups based on learning outcomes. After grouped, there are 3 people in the low category, 27 in the medium category and over 70% in the high category.Abstrak. Analisis Cluster K-Means dalam Pengelompokan Kemampuan Mahasiswa. Pene-litian ini bertujuan untuk mengelompokkan mahasiswa berdasarkan hasil belajar mata kuliah dasar-dasar statistika (DDS yang diukur berdasarkan variabel nilai kehadiran, tugas, ujian tengah semester (UTS, dan ujian akhir semester (UAS untuk selanjutnya digunakan untuk mengevaluasi pembelajaran untuk mata kuliah yang membutuhkan kemampuan analisis kuantititatif yang baik. Penelitian ini menggunakan analisis cluster k-means dalam mengelompokkan mahasiswa ke dalam tiga kelompok berdasarkan hasil belajarnya. Seteleh dikelompokkan, terdapat 3 orang yang masuk pada kategori rendah, 27 orang pada kategori sedang dan lebih dari 70% pada kategori tinggi.Kata Kunci: Cluster Analysis, K-Means Algoritma, Klasifikasi Mahasiswa, Universitas Cokroaminoto Palopo

Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

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Pradipta Maji

Full Text Available Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

Markowitz portfolio optimization model employing fuzzy measure

Science.gov (United States)

Ramli, Suhailywati; Jaaman, Saiful Hafizah

2017-04-01

Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

Science.gov (United States)

Zhang, Jian-Hua; Peng, Xiao-Di; Liu, Hua; Raisch, Jörg; Wang, Ru-Bin

2013-12-01

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

Data Clustering

Science.gov (United States)

Wagstaff, Kiri L.

2012-03-01

matrices—cases in which only pairwise information is known. The list of algorithms covered in this chapter is representative of those most commonly in use, but it is by no means comprehensive. There is an extensive collection of existing books on clustering that provide additional background and depth. Three early books that remain useful today are Anderberg’s Cluster Analysis for Applications [3], Hartigan’s Clustering Algorithms [25], and Gordon’s Classification [22]. The latter covers basics on similarity measures, partitioning and hierarchical algorithms, fuzzy clustering, overlapping clustering, conceptual clustering, validations methods, and visualization or data reduction techniques such as principal components analysis (PCA),multidimensional scaling, and self-organizing maps. More recently, Jain et al. provided a useful and informative survey [27] of a variety of different clustering algorithms, including those mentioned here as well as fuzzy, graph-theoretic, and evolutionary clustering. Everitt’s Cluster Analysis [19] provides a modern overview of algorithms, similarity measures, and evaluation methods.

Estimation of collapse moment for the wall-thinned pipe bends using fuzzy model identification

International Nuclear Information System (INIS)

Na, Man Gyun; Kim, Jin Weon; Hwang, In Joon

2006-01-01

In this work, the collapse moment due to wall-thinned defects is estimated through fuzzy model identification. A subtractive clustering method is used as the basis of a fast and robust algorithm for identifying the fuzzy model. The fuzzy model is optimized by a genetic algorithm combined with a least squares method. The developed fuzzy model has been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy model to reduce the sensitivity to the input change and the fuzzy model are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, three fuzzy models are trained, respectively, for three data sets divided into the three classes of extrados, intrados, and crown defects, which is because they have different characteristics. The relative root mean square (RMS) errors of the estimated collapse moment are 0.5397% for the training data and 0.8673% for the test data. It is known from this result that the fuzzy models are sufficiently accurate to be used in the integrity evaluation of wall-thinned pipe bends and elbows

Clustering Using Boosted Constrained k-Means Algorithm

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Masayuki Okabe

2018-03-01

Full Text Available This article proposes a constrained clustering algorithm with competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learning-based methods. Since it simply adds a function into the data assignment process of the k-means algorithm to check for constraint violations, it often exploits only a small number of constraints. Metric learning-based methods, which exploit constraints to create a new metric for data similarity, have shown promising results although the methods proposed so far are often slow depending on the amount of data or number of feature dimensions. We present a method that exploits the advantages of the constrained k-means and metric learning approaches. It incorporates a mechanism for accepting constraint priorities and a metric learning framework based on the boosting principle into a constrained k-means algorithm. In the framework, a metric is learned in the form of a kernel matrix that integrates weak cluster hypotheses produced by the constrained k-means algorithm, which works as a weak learner under the boosting principle. Experimental results for 12 data sets from 3 data sources demonstrated that our method has performance competitive to those of state-of-the-art constrained clustering methods for most data sets and that it takes much less computation time. Experimental evaluation demonstrated the effectiveness of controlling the constraint priorities by using the boosting principle and that our constrained k-means algorithm functions correctly as a weak learner of boosting.

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network

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Vitor Badiale Furlong

2013-02-01

Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.

Canonical PSO Based K-Means Clustering Approach for Real Datasets.

Science.gov (United States)

Dey, Lopamudra; Chakraborty, Sanjay

2014-01-01

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

Adaptive density trajectory cluster based on time and space distance

Science.gov (United States)

Liu, Fagui; Zhang, Zhijie

2017-10-01

There are some hotspot problems remaining in trajectory cluster for discovering mobile behavior regularity, such as the computation of distance between sub trajectories, the setting of parameter values in cluster algorithm and the uncertainty/boundary problem of data set. As a result, based on the time and space, this paper tries to define the calculation method of distance between sub trajectories. The significance of distance calculation for sub trajectories is to clearly reveal the differences in moving trajectories and to promote the accuracy of cluster algorithm. Besides, a novel adaptive density trajectory cluster algorithm is proposed, in which cluster radius is computed through using the density of data distribution. In addition, cluster centers and number are selected by a certain strategy automatically, and uncertainty/boundary problem of data set is solved by designed weighted rough c-means. Experimental results demonstrate that the proposed algorithm can perform the fuzzy trajectory cluster effectively on the basis of the time and space distance, and obtain the optimal cluster centers and rich cluster results information adaptably for excavating the features of mobile behavior in mobile and sociology network.

An extended k-means technique for clustering moving objects

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Omnia Ossama

2011-03-01

Full Text Available k-means algorithm is one of the basic clustering techniques that is used in many data mining applications. In this paper we present a novel pattern based clustering algorithm that extends the k-means algorithm for clustering moving object trajectory data. The proposed algorithm uses a key feature of moving object trajectories namely, its direction as a heuristic to determine the different number of clusters for the k-means algorithm. In addition, we use the silhouette coefficient as a measure for the quality of our proposed approach. Finally, we present experimental results on both real and synthetic data that show the performance and accuracy of our proposed technique.

*K-means and Cluster Models for Cancer Signatures

OpenAIRE

Kakushadze, Zura; Yu, Willie

2017-01-01

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means’ computational cost is a fraction of NMF’s. Using 1389 published samples for 14 cancer types, we find that 3 cancer...

Fuzzy randomness uncertainty in civil engineering and computational mechanics

CERN Document Server

Möller, Bernd

2004-01-01

This book, for the first time, provides a coherent, overall concept for taking account of uncertainty in the analysis, the safety assessment, and the design of structures. The reader is introduced to the problem of uncertainty modeling and familiarized with particular uncertainty models. For simultaneously considering stochastic and non-stochastic uncertainty the superordinated uncertainty model fuzzy randomness, which contains real valued random variables as well as fuzzy variables as special cases, is presented. For this purpose basic mathematical knowledge concerning the fuzzy set theory and the theory of fuzzy random variables is imparted. The body of the book comprises the appropriate quantification of uncertain structural parameters, the fuzzy and fuzzy probabilistic structural analysis, the fuzzy probabilistic safety assessment, and the fuzzy cluster structural design. The completely new algorithms are described in detail and illustrated by way of demonstrative examples.

K­MEANS CLUSTERING FOR HIDDEN MARKOV MODEL

NARCIS (Netherlands)

Perrone, M.P.; Connell, S.D.

2004-01-01

An unsupervised k­means clustering algorithm for hidden Markov models is described and applied to the task of generating subclass models for individual handwritten character classes. The algorithm is compared to a related clustering method and shown to give a relative change in the error rate of as

Kajian Data Mining Customer Relationship Management pada Lembaga Keuangan Mikro

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Tikaridha Hardiani

2016-01-01

Full Text Available Companies are required to be ready to face the competition will be intense with other companies, including micro-finance institutions. Faced more intense competition, has led to many businesses in microfinance institutions find profitable strategy to distinguish from the others. Strategy that can be applied is implementing Customer Relationship Management (CRM and data mining. Data mining can be used to microfinance institutions that have a large enough data. Determine the potential customers with customer segmentation can help the decision-making marketing strategy that will be implemented . This paper discusses several data mining techniques that can be used for customer segmentation. Proposed method of data mining technique is fuzzy clustering with fuzzy C-Means algorithm and fuzzy RFM. Keywords : Customer relationship management; Data mining; Fuzzy clustering; Micro-finance institutions; Fuzzy C-Means; Fuzzy RFM

Optimasi Pusat Cluster Awal K-Means dengan Algoritma Genetika Pada Pengelompokan Dokumen

OpenAIRE

Fauzi, Muhammad

2017-01-01

147038065 Clustering a data set of documents based on certain data points in documents are an easy way to organize document for extension to work. K-Means clustering algorithm is one of iterative cluster algorithm to partition a set of entities into K cluster. Unfortunately, resulting in K?Means cluster is depending on the initial cluster center that generally assigned randomly. In this reserach, determining initial cluster center K-Means for documents clustering are investi...

A multiresolutional approach to fuzzy text meaning: A first attempt

Energy Technology Data Exchange (ETDEWEB)

Mehler, A.

1996-12-31

The present paper focuses on the connotative meaning aspect of language signs especially above the level of words. In this context the view is taken that texts can be defined as a kind of supersign, to which-in the same way as to other signs-a meaning can be assigned. A text can therefore be described as the result of a sign articulation which connects the material text sign with a corresponding meaning. For the constitution of the structural text meaning a kind of a semiotic composition principle is responsible, which leads to the emergence of interlocked levels of language units, demonstrating different grades of resolution. Starting on the level of words, and going through the level of sentences this principle reaches finally the level of texts by aggregating step by step the meaning of a unit on a higher level out of the meanings of all components one level below, which occur within this unit. Besides, this article will elaborate the hypothesis that the meaning constitution as a two-stage process, corresponding to the syntagmatic and paradigmatic restrictions of language elements among each other, obtains equally on the level of texts. On text level this two-levelledness leads to the constitution of the connotative text meaning, whose constituents are determined on word level by the syntagmatic and paradigmatic relations of the words. The formalization of the text meaning representation occurs with the help of fuzzy set theory.

Fuzzy Control Tutorial

DEFF Research Database (Denmark)

Dotoli, M.; Jantzen, Jan

1999-01-01

The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....

Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means

Science.gov (United States)

Yangmin, GUO; Yun, TANG; Yu, DU; Shisong, TANG; Lianbo, GUO; Xiangyou, LI; Yongfeng, LU; Xiaoyan, ZENG

2018-06-01

Laser-induced breakdown spectroscopy (LIBS) combined with K-means algorithm was employed to automatically differentiate industrial polymers under atmospheric conditions. The unsupervised learning algorithm K-means were utilized for the clustering of LIBS dataset measured from twenty kinds of industrial polymers. To prevent the interference from metallic elements, three atomic emission lines (C I 247.86 nm , H I 656.3 nm, and O I 777.3 nm) and one molecular line C–N (0, 0) 388.3 nm were used. The cluster analysis results were obtained through an iterative process. The Davies–Bouldin index was employed to determine the initial number of clusters. The average relative standard deviation values of characteristic spectral lines were used as the iterative criterion. With the proposed approach, the classification accuracy for twenty kinds of industrial polymers achieved 99.6%. The results demonstrated that this approach has great potential for industrial polymers recycling by LIBS.

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

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Qiang Guo

2018-01-01

Full Text Available In modern electronic warfare, multiple input multiple output (MIMO radar has become an important tool for electronic reconnaissance and intelligence transmission because of its anti-stealth, high resolution, low intercept and anti-destruction characteristics. As a common MIMO radar signal, discrete frequency coding waveform (DFCW has a serious overlap of both time and frequency, so it cannot be directly used in the current radar signal separation problems. The existing fuzzy clustering algorithms have problems in initial value selection, low convergence rate and local extreme values which will lead to the low accuracy of the mixing matrix estimation. Consequently, a novel mixing matrix estimation algorithm based on data field and improved fuzzy C-means (FCM clustering is proposed. First of all, the sparsity and linear clustering characteristics of the time–frequency domain MIMO radar signals are enhanced by using the single-source principal value of complex angular detection. Secondly, the data field uses the potential energy information to analyze the particle distribution, thus design a new clustering number selection scheme. Then the particle swarm optimization algorithm is introduced to improve the iterative clustering process of FCM, and finally get the estimated value of the mixing matrix. The simulation results show that the proposed algorithm improves both the estimation accuracy and the robustness of the mixing matrix.

Nanocomposite metal/plasma polymer films prepared by means of gas aggregation cluster source

Energy Technology Data Exchange (ETDEWEB)

Polonskyi, O.; Solar, P.; Kylian, O.; Drabik, M.; Artemenko, A.; Kousal, J.; Hanus, J.; Pesicka, J.; Matolinova, I. [Charles University in Prague, Faculty of Mathematics and Physics, V Holesovickach 2, 18000 Prague 8 (Czech Republic); Kolibalova, E. [Tescan, Libusina trida 21, 632 00 Brno (Czech Republic); Slavinska, D. [Charles University in Prague, Faculty of Mathematics and Physics, V Holesovickach 2, 18000 Prague 8 (Czech Republic); Biederman, H., E-mail: bieder@kmf.troja.mff.cuni.cz [Charles University in Prague, Faculty of Mathematics and Physics, V Holesovickach 2, 18000 Prague 8 (Czech Republic)

2012-04-02

Nanocomposite metal/plasma polymer films have been prepared by simultaneous plasma polymerization using a mixture of Ar/n-hexane and metal cluster beams. A simple compact cluster gas aggregation source is described and characterized with emphasis on the determination of the amount of charged clusters and their size distribution. It is shown that the fraction of neutral, positively and negatively charged nanoclusters leaving the gas aggregation source is largely influenced by used operational conditions. In addition, it is demonstrated that a large portion of Ag clusters is positively charged, especially when higher currents are used for their production. Deposition of nanocomposite Ag/C:H plasma polymer films is described in detail by means of cluster gas aggregation source. Basic characterization of the films is performed using transmission electron microscopy, ultraviolet-visible and Fourier-transform infrared spectroscopies. It is shown that the morphology, structure and optical properties of such prepared nanocomposites differ significantly from the ones fabricated by means of magnetron sputtering of Ag target in Ar/n-hexane mixture.

Parallel fuzzy connected image segmentation on GPU.

Science.gov (United States)

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K; Miller, Robert W

2011-07-01

Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

Clustering performance comparison using K-means and expectation maximization algorithms.

Science.gov (United States)

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

The global kernel k-means algorithm for clustering in feature space.

Science.gov (United States)

Tzortzis, Grigorios F; Likas, Aristidis C

2009-07-01

Kernel k-means is an extension of the standard k -means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage, through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters, and, due to its incremental nature and search procedure, locates near-optimal solutions avoiding poor local minima. Furthermore, two modifications are developed to reduce the computational cost that do not significantly affect the solution quality. The proposed methods are extended to handle weighted data points, which enables their application to graph partitioning. We experiment with several data sets and the proposed approach compares favorably to kernel k -means with random restarts.

Analysis and research of influence factors ranking of fuzzy language translation accuracy in literary works based on catastrophe progression method

Directory of Open Access Journals (Sweden)

Wei Dong

2017-02-01

Full Text Available This paper researches the problem of decline in translation accuracy caused by language “vagueness” in literary translation, and proposes to use the catastrophe model for importance ranking of various factors affecting the fuzzy language translation accuracy in literary works, and finally gives out the order of factors to be considered before translation. The multi-level evaluation system can be used to construct the relevant catastrophe progression model, and the normalization formula can be used to calculate the relative membership degree of each system and evaluation index, and make evaluation combined with the evaluation criteria table. The results show that, in the fuzzy language translation, in order to improve the translation accuracy, there is a need to consider the indicators ranking: A2 fuzzy language context → A1 words attribute → A3 specific meaning of digital words; B2 fuzzy semantics, B3 blur color words → B1 multiple meanings of words → B4 fuzzy digital words; C3 combination with context and cultural background, C4 specific connotation of color words → C1 combination with words emotion, C2 selection of words meaning → C5 combination with digits and language background.

Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic

DEFF Research Database (Denmark)

Jensen, E W; Nebot, A; Caminal, P

1999-01-01

The aim of this study was to identify a possible relationship between haemodynamic variables, auditory evoked potentials (AEP) and inspired fraction of isoflurane (ISOFl). Two different models (isoflurane and mean arterial pressure) were identified using the fuzzy inductive reasoning (FIR...

Fuzzy Arden Syntax: A fuzzy programming language for medicine.

Science.gov (United States)

Vetterlein, Thomas; Mandl, Harald; Adlassnig, Klaus-Peter

2010-05-01

The programming language Arden Syntax has been optimised for use in clinical decision support systems. We describe an extension of this language named Fuzzy Arden Syntax, whose original version was introduced in S. Tiffe's dissertation on "Fuzzy Arden Syntax: Representation and Interpretation of Vague Medical Knowledge by Fuzzified Arden Syntax" (Vienna University of Technology, 2003). The primary aim is to provide an easy means of processing vague or uncertain data, which frequently appears in medicine. For both propositional and number data types, fuzzy equivalents have been added to Arden Syntax. The Boolean data type was generalised to represent any truth degree between the two extremes 0 (falsity) and 1 (truth); fuzzy data types were introduced to represent fuzzy sets. The operations on truth values and real numbers were generalised accordingly. As the conditions to decide whether a certain programme unit is executed or not may be indeterminate, a Fuzzy Arden Syntax programme may split. The data in the different branches may be optionally aggregated subsequently. Fuzzy Arden Syntax offers the possibility to formulate conveniently Medical Logic Modules (MLMs) based on the principle of a continuously graded applicability of statements. Furthermore, ad hoc decisions about sharp value boundaries can be avoided. As an illustrative example shows, an MLM making use of the features of Fuzzy Arden Syntax is not significantly more complex than its Arden Syntax equivalent; in the ideal case, a programme handling crisp data remains practically unchanged when compared to its fuzzified version. In the latter case, the output data, which can be a set of weighted alternatives, typically depends continuously from the input data. In typical applications an Arden Syntax MLM can produce a different output after only slight changes of the input; discontinuities are in fact unavoidable when the input varies continuously but the output is taken from a discrete set of possibilities

Fuzzy production planning models for an unreliable production system with fuzzy production rate and stochastic/fuzzy demand rate

Directory of Open Access Journals (Sweden)

K. A. Halim

2011-01-01

Full Text Available In this article, we consider a single-unit unreliable production system which produces a single item. During a production run, the production process may shift from the in-control state to the out-of-control state at any random time when it produces some defective items. The defective item production rate is assumed to be imprecise and is characterized by a trapezoidal fuzzy number. The production rate is proportional to the demand rate where the proportionality constant is taken to be a fuzzy number. Two production planning models are developed on the basis of fuzzy and stochastic demand patterns. The expected cost per unit time in the fuzzy sense is derived in each model and defuzzified by using the graded mean integration representation method. Numerical examples are provided to illustrate the optimal results of the proposed fuzzy models.

Mapping Diversity of Publication Patterns in the Social Sciences and Humanities: An Approach Making Use of Fuzzy Cluster Analysis

Directory of Open Access Journals (Sweden)

Frederik T. Verleysen

2016-11-01

Full Text Available Purpose: To present a method for systematically mapping diversity of publication patterns at the author level in the social sciences and humanities in terms of publication type, publication language and co-authorship. Design/methodology/approach: In a follow-up to the hard partitioning clustering by Verleysen and Weeren in 2016, we now propose the complementary use of fuzzy cluster analysis, making use of a membership coefficient to study gradual differences between publication styles among authors within a scholarly discipline. The analysis of the probability density function of the membership coefficient allows to assess the distribution of publication styles within and between disciplines. Findings: As an illustration we analyze 1,828 productive authors affiliated in Flanders, Belgium. Whereas a hard partitioning previously identified two broad publication styles, an international one vs. a domestic one, fuzzy analysis now shows gradual differences among authors. Internal diversity also varies across disciplines and can be explained by researchers' specialization and dissemination strategies. Research limitations: The dataset used is limited to one country for the years 2000-2011; a cognitive classification of authors may yield a different result from the affiliation-based classification used here. Practical implications: Our method is applicable to other bibliometric and research evaluation contexts, especially for the social sciences and humanities in non-Anglophone countries. Originality/value: The method proposed is a novel application of cluster analysis to the field of bibliometrics. Applied to publication patterns at the author level in the social sciences and humanities, for the first time it systematically documents intra-disciplinary diversity.

Combinational Reasoning of Quantitative Fuzzy Topological Relations for Simple Fuzzy Regions

Science.gov (United States)

Liu, Bo; Li, Dajun; Xia, Yuanping; Ruan, Jian; Xu, Lili; Wu, Huanyi

2015-01-01

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models. PMID:25775452

A diabetic retinopathy detection method using an improved pillar K-means algorithm.

Science.gov (United States)

Gogula, Susmitha Valli; Divakar, Ch; Satyanarayana, Ch; Rao, Allam Appa

2014-01-01

The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with Kmeans and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

Energy Technology Data Exchange (ETDEWEB)

Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina, E-mail: despina.kontos@uphs.upenn.edu [Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States)

2013-12-15

Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

International Nuclear Information System (INIS)

Wu, Shandong; Weinstein, Susan P.; Conant, Emily F.; Kontos, Despina

2013-01-01

Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a correlation ofr = 0

FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

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Pelin GORGEL

2013-01-01

Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

Quantitative estimation of time-variable earthquake hazard by using fuzzy set theory

Science.gov (United States)

Deyi, Feng; Ichikawa, M.

1989-11-01

In this paper, the various methods of fuzzy set theory, called fuzzy mathematics, have been applied to the quantitative estimation of the time-variable earthquake hazard. The results obtained consist of the following. (1) Quantitative estimation of the earthquake hazard on the basis of seismicity data. By using some methods of fuzzy mathematics, seismicity patterns before large earthquakes can be studied more clearly and more quantitatively, highly active periods in a given region and quiet periods of seismic activity before large earthquakes can be recognized, similarities in temporal variation of seismic activity and seismic gaps can be examined and, on the other hand, the time-variable earthquake hazard can be assessed directly on the basis of a series of statistical indices of seismicity. Two methods of fuzzy clustering analysis, the method of fuzzy similarity, and the direct method of fuzzy pattern recognition, have been studied is particular. One method of fuzzy clustering analysis is based on fuzzy netting, and another is based on the fuzzy equivalent relation. (2) Quantitative estimation of the earthquake hazard on the basis of observational data for different precursors. The direct method of fuzzy pattern recognition has been applied to research on earthquake precursors of different kinds. On the basis of the temporal and spatial characteristics of recognized precursors, earthquake hazards in different terms can be estimated. This paper mainly deals with medium-short-term precursors observed in Japan and China.

Implementasi Adaptive Neuro-Fuzzy Inference System (Anfis untuk Peramalan Pemakaian Air di Perusahaan Daerah Air Minum Tirta Moedal Semarang

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Ulfatun Hani'ah

2016-06-01

Full Text Available Peramalan pemakaian air pada bulan januari 2015 sampai April 2015 dapat dilakukan menggunakan perhitungan matematika dengan bantuan ilmu komputer. Metode yang digunakan adalah Adaptive Neuro Fuzzy Inference System (ANFIS dengan bantuan software MATLAB. Untuk pengujian program, dilakukan percobaan dengan memasukkan variabel klas = 2, maksimum epoh = 100, error = 10-6, rentang nilai learning rate = 0.6 sampai 0.9, dan rentang nilai momentum = 0.6 sampai 0.9. Simpulan yang diperoleh adalah bahwa implementasi metode Adaptive Neuro-Fuzzy Inference System dalam peramalan pemakaian air yang pertama adalah membuat rancangan flowchart, melakukan clustering data menggunakan fuzzy C-Mean, menentukan neuron tiap-tiap lapisan, mencari nilai parameter dengan menggunakan LSE rekursif, lalu penentuan perhitungan error menggunakan sum square error (SSE dan membuat sistem peramalan pemakaian air dengan software MATLAB. Setelah dilakukan percobaan hasil yang menunjukkan SSE paling kecil adalah nilai learning rate 0.9 dan momentum 0.6 dengan SSE 0.0080107. Hasil peramalan pemakaian air pada bulan Januari adalah 3.836.138m3, bulan Februari adalah 3.595.188m3, bulan Maret adalah 3.596.416 m3, dan bulan April adalah 3.776.833 m3. 

Development of classification and prediction methods of critical heat flux using fuzzy theory and artificial neural networks

International Nuclear Information System (INIS)

Moon, Sang Ki

1995-02-01

This thesis applies new information techniques, artificial neural networks, (ANNs) and fuzzy theory, to the investigation of the critical heat flux (CHF) phenomenon for water flow in vertical round tubes. The work performed are (a) classification and prediction of CHF based on fuzzy clustering and ANN, (b) prediction and parametric trends analysis of CHF using ANN with the introduction of dimensionless parameters, and (c) detection of CHF occurrence using fuzzy rule and spatiotemporal neural network (STN). Fuzzy clustering and ANN are used for classification and prediction of the CHF using primary system parameters. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulted clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanisms. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. Parametric trends of the CHF are analyzed by applying artificial neural networks to a CHF data base for water flow in uniformly heated vertical round tubes. The analyses are performed from three viewpoints, i.e., for fixed inlet conditions, for fixed exit conditions, and based on local conditions hypothesis. In order to remove the necessity of data classification, Katto and Groeneveld et al.'s dimensionless parameters are introduced in training the ANNs with the experimental CHF data. The trained ANNs predict the CHF better than any other conventional correlations, showing RMS error of 8.9%, 13.1%, and 19.3% for fixed inlet conditions, for fixed exit conditions, and for local

Search for C+ C clustering in Mg ground state

Indian Academy of Sciences (India)

2017-01-04

Jan 4, 2017 ... Finite-range knockout theory predictions were much larger for (12C,212C) reaction, indicating a very small 12C−12C clustering in 24Mg. (g.s.) . Our present results contradict most of the proposed heavy cluster (12C+12C) structure models for the ground state of 24Mg. Keywords. Direct nuclear reactions ...

A new type of simplified fuzzy rule-based system

Science.gov (United States)

Angelov, Plamen; Yager, Ronald

2012-02-01

Over the last quarter of a century, two types of fuzzy rule-based (FRB) systems dominated, namely Mamdani and Takagi-Sugeno type. They use the same type of scalar fuzzy sets defined per input variable in their antecedent part which are aggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed. Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept.

Foundations Of Fuzzy Control

DEFF Research Database (Denmark)

Jantzen, Jan

The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

Science.gov (United States)

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Approximate Solution of LR Fuzzy Sylvester Matrix Equations

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Xiaobin Guo

2013-01-01

Full Text Available The fuzzy Sylvester matrix equation AX~+X~B=C~ in which A,B are m×m and n×n crisp matrices, respectively, and C~ is an m×n LR fuzzy numbers matrix is investigated. Based on the Kronecker product of matrices, we convert the fuzzy Sylvester matrix equation into an LR fuzzy linear system. Then we extend the fuzzy linear system into two systems of linear equations according to the arithmetic operations of LR fuzzy numbers. The fuzzy approximate solution of the original fuzzy matrix equation is obtained by solving the crisp linear systems. The existence condition of the LR fuzzy solution is also discussed. Some examples are given to illustrate the proposed method.

Automatic detection of erythemato-squamous diseases using k-means clustering.

Science.gov (United States)

Ubeyli, Elif Derya; Doğdu, Erdoğan

2010-04-01

A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.

Diagnostics of D.C. Starter Generators While Using Fuzzy Logic Theory

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A. A. Lobaty

2011-01-01

Full Text Available Methodology for sparking control in generator brush contact has been developed on the basis of the proposed fuzzy diagnostic model for ad.c.starter-generator and complex processing of diagnostic data. The paper provides investigation results, namely: dependence of voltage parameters  in an external generator circuit on collector sparking intensity.

A heart disease recognition embedded system with fuzzy cluster algorithm.

Science.gov (United States)

de Carvalho, Helton Hugo; Moreno, Robson Luiz; Pimenta, Tales Cleber; Crepaldi, Paulo C; Cintra, Evaldo

2013-06-01

This article presents the viability analysis and the development of heart disease identification embedded system. It offers a time reduction on electrocardiogram - ECG signal processing by reducing the amount of data samples, without any significant loss. The goal of the developed system is the analysis of heart signals. The ECG signals are applied into the system that performs an initial filtering, and then uses a Gustafson-Kessel fuzzy clustering algorithm for the signal classification and correlation. The classification indicated common heart diseases such as angina, myocardial infarction and coronary artery diseases. The system uses the European electrocardiogram ST-T Database (EDB) as a reference for tests and evaluation. The results prove the system can perform the heart disease detection on a data set reduced from 213 to just 20 samples, thus providing a reduction to just 9.4% of the original set, while maintaining the same effectiveness. This system is validated in a Xilinx Spartan(®)-3A FPGA. The field programmable gate array (FPGA) implemented a Xilinx Microblaze(®) Soft-Core Processor running at a 50MHz clock rate. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A Variable-Selection Heuristic for K-Means Clustering.

Science.gov (United States)

Brusco, Michael J.; Cradit, J. Dennis

2001-01-01

Presents a variable selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. Subjected the heuristic to Monte Carlo testing across more than 2,200 datasets. Results indicate that the heuristic is extremely effective at eliminating masking variables. (SLD)

Developing cluster strategy of apples dodol SMEs by integration K-means clustering and analytical hierarchy process method

Science.gov (United States)

Mustaniroh, S. A.; Effendi, U.; Silalahi, R. L. R.; Sari, T.; Ala, M.

2018-03-01

The purposes of this research were to determine the grouping of apples dodol small and medium enterprises (SMEs) in Batu City and to determine an appropriate development strategy for each cluster. The methods used for clustering SMEs was k-means. The Analytical Hierarchy Process (AHP) approach was then applied to determine the development strategy priority for each cluster. The variables used in grouping include production capacity per month, length of operation, investment value, average sales revenue per month, amount of SMEs assets, and the number of workers. Several factors were considered in AHP include industry cluster, government, as well as related and supporting industries. Data was collected using the methods of questionaire and interviews. SMEs respondents were selected among SMEs appels dodol in Batu City using purposive sampling. The result showed that two clusters were formed from five apples dodol SMEs. The 1stcluster of apples dodol SMEs, classified as small enterprises, included SME A, SME C, and SME D. The 2ndcluster of SMEs apples dodol, classified as medium enterprises, consisted of SME B and SME E. The AHP results indicated that the priority development strategy for the 1stcluster of apples dodol SMEs was improving quality and the product standardisation, while for the 2nd cluster was increasing the marketing access.

A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor

International Nuclear Information System (INIS)

Zio, Enrico; Baraldi, Piero; Popescu, Irina Crenguta

2009-01-01

This paper extends a method previously introduced by the authors for building a transparent fault classification algorithm by combining the fuzzy clustering, fuzzy logic and decision trees techniques. The baseline method transforms an opaque, fuzzy clustering-based classification model into a fuzzy logic inference model based on linguistic rules which can be represented by a decision tree formalism. The classification model thereby obtained is transparent in that it allows direct interpretation and inspection of the model. An extension in the procedure for the development of the fuzzy logic inference model is introduced to allow the treatment of more complicated cases, e.g. splitted and overlapping clusters. The corresponding computational tool developed relies on a number of parameters which can be tuned by the user to optimally compromise the level of transparency of the classification process and its efficiency. A numerical application is presented with regards to the fault classification in the Steam Generator of a Pressurized Water Reactor.

Optimization of the test intervals of a nuclear safety system by genetic algorithms, solution clustering and fuzzy preference assignment

International Nuclear Information System (INIS)

Zio, E.; Bazzo, R.

2010-01-01

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into 'families'. On the basis of the decision maker's preferences, each family is then synthetically represented by a 'head of the family' solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions

Decomposition of fuzzy continuity and fuzzy ideal continuity via fuzzy idealization

International Nuclear Information System (INIS)

Zahran, A.M.; Abbas, S.E.; Abd El-baki, S.A.; Saber, Y.M.

2009-01-01

Recently, El-Naschie has shown that the notion of fuzzy topology may be relevant to quantum paretical physics in connection with string theory and E-infinity space time theory. In this paper, we study the concepts of r-fuzzy semi-I-open, r-fuzzy pre-I-open, r-fuzzy α-I-open and r-fuzzy β-I-open sets, which is properly placed between r-fuzzy openness and r-fuzzy α-I-openness (r-fuzzy pre-I-openness) sets regardless the fuzzy ideal topological space in Sostak sense. Moreover, we give a decomposition of fuzzy continuity, fuzzy ideal continuity and fuzzy ideal α-continuity, and obtain several characterization and some properties of these functions. Also, we investigate their relationship with other types of function.

Merging K-means with hierarchical clustering for identifying general-shaped groups.

Science.gov (United States)

Peterson, Anna D; Ghosh, Arka P; Maitra, Ranjan

2018-01-01

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K -means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while K -means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using K -means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

Science.gov (United States)

Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

2010-10-01

To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

An improved K-means clustering algorithm in agricultural image segmentation

Science.gov (United States)

Cheng, Huifeng; Peng, Hui; Liu, Shanmei

Image segmentation is the first important step to image analysis and image processing. In this paper, according to color crops image characteristics, we firstly transform the color space of image from RGB to HIS, and then select proper initial clustering center and cluster number in application of mean-variance approach and rough set theory followed by clustering calculation in such a way as to automatically segment color component rapidly and extract target objects from background accurately, which provides a reliable basis for identification, analysis, follow-up calculation and process of crops images. Experimental results demonstrate that improved k-means clustering algorithm is able to reduce the computation amounts and enhance precision and accuracy of clustering.

Recognition of Handwritten Arabic words using a neuro-fuzzy network

International Nuclear Information System (INIS)

Boukharouba, Abdelhak; Bennia, Abdelhak

2008-01-01

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database

Estimating microalgae Synechococcus nidulans daily biomass concentration using neuro-fuzzy network Estimador neuro-fuzzy de concentração diária de biomassa da microalga Synechococcus nidulans

Directory of Open Access Journals (Sweden)

Vitor Badiale Furlong

2013-02-01

Full Text Available In this study, a neuro-fuzzy estimator was developed for the estimation of biomass concentration of the microalgae Synechococcus nidulans from initial batch concentrations, aiming to predict daily productivity. Nine replica experiments were performed. The growth was monitored daily through the culture medium optic density and kept constant up to the end of the exponential phase. The network training followed a full 3³ factorial design, in which the factors were the number of days in the entry vector (3,5 and 7 days, number of clusters (10, 30 and 50 clusters and internal weight softening parameter (Sigma (0.30, 0.45 and 0.60. These factors were confronted with the sum of the quadratic error in the validations. The validations had 24 (A and 18 (B days of culture growth. The validations demonstrated that in long-term experiments (Validation A the use of a few clusters and high Sigma is necessary. However, in short-term experiments (Validation B, Sigma did not influence the result. The optimum point occurred within 3 days in the entry vector, 10 clusters and 0.60 Sigma and the mean determination coefficient was 0.95. The neuro-fuzzy estimator proved a credible alternative to predict the microalgae growth.Neste trabalho, foi construído um estimador neuro-fuzzy da concentração de biomassa da microalga Synechococcus nidulans a partir de concentrações iniciais da batelada, visando possibilitar a predição da produtividade. Nove experimentos em réplica foram realizados. O crescimento foi acompanhado diariamente pela transmitância do meio e mantido até o final da fase exponencial de crescimento. O treinamento das redes ocorreu segundo delineamento experimental 3³, os fatores foram o número de dias no vetor de entrada (3, 5 e 7 dias, o número de clusters (10, 30 e 50 clusters e o valor de abrandamento do filtro interno (Sigma (0,30, 0,45 e 0,60. A variável resposta foi o somatório do erro quadrático das validações. Estas possuíam 24 (A

Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification

Directory of Open Access Journals (Sweden)

Yu Feng

2017-01-01

Full Text Available This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.

Support Vector Data Descriptions and k-Means Clustering: One Class?

Science.gov (United States)

Gornitz, Nico; Lima, Luiz Alberto; Muller, Klaus-Robert; Kloft, Marius; Nakajima, Shinichi

2017-09-27

We present ClusterSVDD, a methodology that unifies support vector data descriptions (SVDDs) and k-means clustering into a single formulation. This allows both methods to benefit from one another, i.e., by adding flexibility using multiple spheres for SVDDs and increasing anomaly resistance and flexibility through kernels to k-means. In particular, our approach leads to a new interpretation of k-means as a regularized mode seeking algorithm. The unifying formulation further allows for deriving new algorithms by transferring knowledge from one-class learning settings to clustering settings and vice versa. As a showcase, we derive a clustering method for structured data based on a one-class learning scenario. Additionally, our formulation can be solved via a particularly simple optimization scheme. We evaluate our approach empirically to highlight some of the proposed benefits on artificially generated data, as well as on real-world problems, and provide a Python software package comprising various implementations of primal and dual SVDD as well as our proposed ClusterSVDD.

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

Science.gov (United States)

Brusco, Michael J; Shireman, Emilie; Steinley, Douglas

2017-09-01

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

Directory of Open Access Journals (Sweden)

M.F.M. Yunoh

2015-06-01

Full Text Available This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

Fuzzy promises

DEFF Research Database (Denmark)

Anker, Thomas Boysen; Kappel, Klemens; Eadie, Douglas

2012-01-01

as narrative material to communicate self-identity. Finally, (c) we propose that brands deliver fuzzy experiential promises through effectively motivating consumers to adopt and play a social role implicitly suggested and facilitated by the brand. A promise is an inherently ethical concept and the article...... concludes with an in-depth discussion of fuzzy brand promises as two-way ethical commitments that put requirements on both brands and consumers....

TRUSTWORTHY OPTIMIZED CLUSTERING BASED TARGET DETECTION AND TRACKING FOR WIRELESS SENSOR NETWORK

Directory of Open Access Journals (Sweden)

C. Jehan

2016-06-01

Full Text Available In this paper, an efficient approach is proposed to address the problem of target tracking in wireless sensor network (WSN. The problem being tackled here uses adaptive dynamic clustering scheme for tracking the target. It is a specific problem in object tracking. The proposed adaptive dynamic clustering target tracking scheme uses three steps for target tracking. The first step deals with the identification of clusters and cluster heads using OGSAFCM. Here, kernel fuzzy c-means (KFCM and gravitational search algorithm (GSA are combined to create clusters. At first, oppositional gravitational search algorithm (OGSA is used to optimize the initial clustering center and then the KFCM algorithm is availed to guide the classification and the cluster formation process. In the OGSA, the concept of the opposition based population initialization in the basic GSA to improve the convergence profile. The identified clusters are changed dynamically. The second step deals with the data transmission to the cluster heads. The third step deals with the transmission of aggregated data to the base station as well as the detection of target. From the experimental results, the proposed scheme efficiently and efficiently identifies the target. As a result the tracking error is minimized.

Finding reproducible cluster partitions for the k-means algorithm.

Science.gov (United States)

Lisboa, Paulo J G; Etchells, Terence A; Jarman, Ian H; Chambers, Simon J

2013-01-01

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

Science.gov (United States)

El-Nagar, Ahmad M

2018-01-01

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Fifty years of fuzzy logic and its applications

CERN Document Server

Rishe, Naphtali; Kandel, Abraham

2015-01-01

This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadeh’s seminal paper on “fuzzy sets,” published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadeh’s paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining, and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years’ anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments...

Performance Analysis of Entropy Methods on K Means in Clustering Process

Science.gov (United States)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

Science.gov (United States)

Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

2016-01-01

In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

A note on the L-fuzzy Banach's contraction principle

International Nuclear Information System (INIS)

Martinez-Moreno, J.; Roldan, A.; Roldan, C.

2009-01-01

Recently, Alaca et al. [Alaca C, Turkoglu D, Yildiz C. Fixed points in intuitionistic fuzzy metric spaces. Chaos, Solitons and Fractals 2006;29:10738] proved fuzzy Banach fixed point theorem in intuitionistic fuzzy metric spaces and Saadati [Saadati R. Notes to the paper 'fixed points in intuitionistic fuzzy metric spaces' and its generalization to L-fuzzy metric spaces. Chaos, Solitions and Fractals 2008;35:80-176] extended it in generalized fuzzy metric spaces. The purpose of this paper is to give a correct proof of the main result in Saadati [Saadati R. Notes to the paper 'fixed points in intuitionistic fuzzy metric spaces' and its generalization to L-fuzzy metric spaces. Chaos, Solitions and Fractals 2008;35:80-176].

Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

Directory of Open Access Journals (Sweden)

Ganchimeg Ganbold

2017-03-01

Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

Optimizing biological waste water cleaning by means of modern control systems (fuzzy logic); Optimierung der biologischen Abwasserreinigung durch moderne Regelsysteme (Fuzzy-Logik)

Energy Technology Data Exchange (ETDEWEB)

Lohse, M.; Boening, T.; Hegemann, G. [Fachhochschule Muenster (Germany). Inst. fuer Abfall- und Abwasserwirtschaft e.V.

1999-07-01

Within the framework of a project sponsored by EUREGIO, test series with the biological activation stages of a German and a Dutch sewage treatment plant each are carried out using different process concepts for the control of oxygen supply by fuzzy logic. As the currently available results demonstrate, the developed fuzzy-logic fields of characteristic curves permit establishing a stable and, thus, little energy-consuming process with optimum oxygen supply in comparison with conventional control. (orig.) [German] Im Rahmen eines von der EUREGIO gefoerderten Forschungsprojektes werden Versuchsreihen im Bereich der biologischen Belebungsstufen einer deutschen und einer niederlaendischen Abwasserreinigungsanlage (ARA) mit unterschiedlichen Verfahrenskonzepten hinsichtlich der Regelung der Sauerstoffzufuhr mit Hilfe der Fuzzy-Logik Technik durchgefuehrt. Die bisherigen Versuchsergebnisse zeigen, dass - im Vergleich zur konventionellen Regelung - durch die entwickelten Fuzzy-Logik Kennfelder ein stabiler und damit energiearmer Prozess mit optimaler Sauerstoffzufuhr erzeugt wird. (orig.)

Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

CERN Document Server

Keller, James M; Fogel, David B

2016-01-01

This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

Using adaptive neuro-fuzzy inference system technique for crosstalk correction in simultaneous {sup 99m}Tc/{sup 201}Tl SPECT imaging: A Monte Carlo simulation study

Energy Technology Data Exchange (ETDEWEB)

Heidary, Saeed, E-mail: saeedheidary@aut.ac.ir; Setayeshi, Saeed, E-mail: setayesh@aut.ac.ir

2015-01-11

This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous {sup 99m}Tc/{sup 201}Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of {sup 201}Tl (77±10% keV) and {sup 99m}Tc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.

Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

Science.gov (United States)

Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2017-08-24

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

Fuzzy statistical decision-making theory and applications

CERN Document Server

Kabak, Özgür

2016-01-01

This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimu...

A SELF-ORGANISING FUZZY LOGIC CONTROLLER

African Journals Online (AJOL)

ES Obe

One major drawback of fuzzy logic controllers is the difficulty encountered in the construction of a rule- base ... The greatest limitation of fuzzy logic control is the lack ..... c(kT)= e(kT)-e((k-1)T). (16) .... with the aid of fuzzy models”, It in Industrial.

Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm

Directory of Open Access Journals (Sweden)

Wei Huang

2013-01-01

Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.

Business Planning in the Light of Neuro-fuzzy and Predictive Forecasting

Science.gov (United States)

Chakrabarti, Prasun; Basu, Jayanta Kumar; Kim, Tai-Hoon

In this paper we have pointed out gain sensing on forecast based techniques.We have cited an idea of neural based gain forecasting. Testing of sequence of gain pattern is also verifies using statsistical analysis of fuzzy value assignment. The paper also suggests realization of stable gain condition using K-Means clustering of data mining. A new concept of 3D based gain sensing has been pointed out. The paper also reveals what type of trend analysis can be observed for probabilistic gain prediction.

Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design.

Science.gov (United States)

Mata, Edson; Bandeira, Silvio; de Mattos Neto, Paulo; Lopes, Waslon; Madeiro, Francisco

2016-11-23

The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms.

Fuzzy Bi-level Decision-Making Techniques: A Survey

Directory of Open Access Journals (Sweden)

Guangquan Zhang

2016-04-01

Full Text Available Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques.

A NEW APPROACH ON SHORTEST PATH IN FUZZY ENVIRONMENT

OpenAIRE

A. Nagoorgani; A. Mumtaj Begam

2010-01-01

This paper introduces a new type of fuzzy shortest path network problem using triangular fuzzy number. To find the smallest edge by the fuzzy distance using graded mean integration representation of generalized fuzzy number for every node. Thus the optimum shortest path for the given problem is obtained.

Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

Directory of Open Access Journals (Sweden)

Jinhua Li

2016-01-01

Full Text Available Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and K-means. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms.

Systematic methods for the design of a class of fuzzy logic controllers

Science.gov (United States)

Yasin, Saad Yaser

2002-09-01

Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental

A fuzzy method for improving the functionality of search engines based on user's web interactions

Directory of Open Access Journals (Sweden)

Farzaneh Kabirbeyk

2015-04-01

Full Text Available Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided.

An intelligent clustering based methodology for confusable diseases ...

African Journals Online (AJOL)

Journal of Computer Science and Its Application ... In this paper, an intelligent system driven by fuzzy clustering algorithm and Adaptive Neuro-Fuzzy Inference System for ... Data on patients diagnosed and confirmed by laboratory tests of viral ...

Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique

Science.gov (United States)

Srivastava, Gaurav; Panda, Sudhindra N.; Mondal, Pratap; Liu, Junguo

2010-12-01

SummaryForecasting of rainfall is imperative for rainfed agriculture of arid and semi-arid regions of the world where agriculture consumes nearly 80% of the total water demand. Fuzzy-Ranking Algorithm (FRA) is used to identify the significant input variables for rainfall forecast. A case study is carried out to forecast monthly rainfall in India with several ocean-atmospheric predictor variables. Three different scenarios of ocean-atmospheric predictor variables are used as a set of possible input variables for rainfall forecasting model: (1) two climate indices, i.e. Southern Oscillation Index (SOI) and Pacific Decadal Oscillation Index (PDOI); (2) Sea Surface Temperature anomalies (SSTa) in the 5° × 5° grid points in Indian Ocean; and (3) both the climate indices and SSTa. To generate a set of possible input variables for these scenarios, we use climatic indices and the SSTa data with different lags between 1 and 12 months. Nonlinear relationship between identified inputs and rainfall is captured with an Artificial Neural Network (ANN) technique. A new approach based on fuzzy c-mean clustering is proposed for dividing data into representative subsets for training, testing, and validation. The results show that this proposed approach overcomes the difficulty in determining optimal numbers of clusters associated with the data division technique of self-organized map. The ANN model developed with both the climate indices and SSTa shows the best performance for the forecast of the monthly August rainfall in India. Similar approach can be applied to forecast rainfall of any period at selected climatic regions of the world where significant relationship exists between the rainfall and climate indices.

Microgrids Real-Time Pricing Based on Clustering Techniques

Directory of Open Access Journals (Sweden)

Hao Liu

2018-05-01

Full Text Available Microgrids are widely spreading in electricity markets worldwide. Besides the security and reliability concerns for these microgrids, their operators need to address consumers’ pricing. Considering the growth of smart grids and smart meter facilities, it is expected that microgrids will have some level of flexibility to determine real-time pricing for at least some consumers. As such, the key challenge is finding an optimal pricing model for consumers. This paper, accordingly, proposes a new pricing scheme in which microgrids are able to deploy clustering techniques in order to understand their consumers’ load profiles and then assign real-time prices based on their load profile patterns. An improved weighted fuzzy average k-means is proposed to cluster load curve of consumers in an optimal number of clusters, through which the load profile of each cluster is determined. Having obtained the load profile of each cluster, real-time prices are given to each cluster, which is the best price given to all consumers in that cluster.

4C radio sources in clusters of galaxies

International Nuclear Information System (INIS)

McHardy, I.M.

1979-01-01

Observations of a complete sample of 4C and 4CT radio sources in Abell clusters with the Cambridge One-Mile telescope are analysed. It is concluded that radio sources are strongly concentrated towards the cluster centres and are equally likely to be found in clusters of any richness. The probability of a galaxy of a given absolute magnitude producing a source above a given luminosity does not depend on cluster membership. 4C and 4CT radio sources in clusters, selected at 178 MHz, occur preferentially in Bautz-Morgan (BM) class 1 clusters, whereas those selected at 1.4 GHz do not. The most powerful radio source in the cluster is almost always associated with the optically brightest galaxy. The average spectrum of 4C sources in the range 408 to 1407 MHz is steeper in BM class 1 than in other classes. Spectra also steepen with cluster richness. the morphology of 4C sources in clusters depends strongly on BM class and, in particular, radio-trail sources occur only in BM classes II, II-III and III. (author)

Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

International Nuclear Information System (INIS)

Moon, Sang Ki; Chang, Soon Heung

1994-01-01

A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. ((orig.))

A New Soft Computing Method for K-Harmonic Means Clustering.

Science.gov (United States)

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

Security and Correctness Analysis on Privacy-Preserving k-Means Clustering Schemes

Science.gov (United States)

Su, Chunhua; Bao, Feng; Zhou, Jianying; Takagi, Tsuyoshi; Sakurai, Kouichi

Due to the fast development of Internet and the related IT technologies, it becomes more and more easier to access a large amount of data. k-means clustering is a powerful and frequently used technique in data mining. Many research papers about privacy-preserving k-means clustering were published. In this paper, we analyze the existing privacy-preserving k-means clustering schemes based on the cryptographic techniques. We show those schemes will cause the privacy breach and cannot output the correct results due to the faults in the protocol construction. Furthermore, we analyze our proposal as an option to improve such problems but with intermediate information breach during the computation.

A heuristic approach to possibilistic clustering algorithms and applications

CERN Document Server

Viattchenin, Dmitri A

2013-01-01

The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects.   The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover,  a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientist...

Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

Science.gov (United States)

de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

CLUSTERING PENENTUAN POTENSI KEJAHATAN DAERAH DI KOTA BANJARBARU DENGAN METODE K-MEANS

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Sri Rahayu

2016-09-01

Full Text Available Abstract Within the scope of the police, the data held in the database can be used to make a crime report, the presumption of evil to come, and so on. With the data mining based on the amount of data stored so much, these data can be processed to find the useful knowledge for police. One technique that is known in the data mining clustering techniques. The purpose of the job grouping (clustering the data can be divided into two, namely grouping for understanding and grouping to use. Methods K-Means clustering is a method for engineering the most simple and common. KMeans clustering is one method of data non-hierarchy (partition which seeks to partition the existing data in the form of two or more groups. This method of partitioning data into groups so that the same characteristic of data put into the same group and a different characteristic data are grouped into another group. The purpose of this grouping is to minimize the objective function is set in the grouping process, which generally seek to minimize the variation within a group and maximize the variation between groups. The data mined to determine the potential clustering of crime in the city area of crime data Banjarbaru is owned by the city police in the Police Banjarbaru. Thus this study aims to assess the stage of clustering techniques and build clustering determination of potential crime areas in the city Banjarbaru. Keywords:Clustering, Data mining, K-Means, K-Means Clustering ABSTRAK Dalam ruang lingkup kepolisian, data-data yang dimiliki pada basis data dapat dimanfaatkan untuk pembuatan laporan kejahatan, praduga kejahatan yang akan datang, dan sebagainya.Dengan adanya data mining yang didasarkan pada jumlah data yang tersimpan begitu banyak, data-data tersebut dapat diproses untuk menemukan suatu pengetahuan yang berguna bagi pihak kepolisian.Salah satu teknik yang dikenal dalam data mining yaitu teknik clustering.Tujuan pekerjaan pengelompokan (clustering data dapat dibedakan

A new method for ordering triangular fuzzy numbers

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S.H. Nasseri

2010-09-01

Full Text Available Ranking fuzzy numbers plays a very important role in linguistic decision making and other fuzzy application systems. In spite of many ranking methods, no one can rank fuzzy numbers with human intuition consistently in all cases. Shortcoming are found in some of the convenient methods for ranking triangular fuzzy numbers such as the coefficient of variation (CV index, distance between fuzzy sets, centroid point and original point, and also weighted mean value. In this paper, we introduce a new method for ranking triangular fuzzy number to overcome the shortcomings of the previous techniques. Finally, we compare our method with some convenient methods for ranking fuzzy numbers to illustrate the advantage our method.

Performance Analysis of Cluster Formation in Wireless Sensor Networks.

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Montiel, Edgar Romo; Rivero-Angeles, Mario E; Rubino, Gerardo; Molina-Lozano, Heron; Menchaca-Mendez, Rolando; Menchaca-Mendez, Ricardo

2017-12-13

Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

Performance Analysis of Cluster Formation in Wireless Sensor Networks

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Edgar Romo Montiel

2017-12-01

Full Text Available Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes.

Clustering for Binary Data Sets by Using Genetic Algorithm-Incremental K-means

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Saharan, S.; Baragona, R.; Nor, M. E.; Salleh, R. M.; Asrah, N. M.

2018-04-01

This research was initially driven by the lack of clustering algorithms that specifically focus in binary data. To overcome this gap in knowledge, a promising technique for analysing this type of data became the main subject in this research, namely Genetic Algorithms (GA). For the purpose of this research, GA was combined with the Incremental K-means (IKM) algorithm to cluster the binary data streams. In GAIKM, the objective function was based on a few sufficient statistics that may be easily and quickly calculated on binary numbers. The implementation of IKM will give an advantage in terms of fast convergence. The results show that GAIKM is an efficient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. In conclusion, the GAIKM outperformed other clustering algorithms such as GCUK, IKM, Scalable K-means (SKM) and K-means clustering and paves the way for future research involving missing data and outliers.

An Initial Seed Selection Algorithm for K-means Clustering of Georeferenced Data to Improve Replicability of Cluster Assignments for Mapping Application

OpenAIRE

Khan, Fouad

2016-01-01

K-means is one of the most widely used clustering algorithms in various disciplines, especially for large datasets. However the method is known to be highly sensitive to initial seed selection of cluster centers. K-means++ has been proposed to overcome this problem and has been shown to have better accuracy and computational efficiency than k-means. In many clustering problems though -such as when classifying georeferenced data for mapping applications- standardization of clustering methodolo...

Determination of interrill soil erodibility coefficient based on Fuzzy and Fuzzy-Genetic Systems

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Habib Palizvan Zand

2017-02-01

independent variables for development fuzzy and fuzzy- genetic models. For this reason their linguistic variables were defined and fuzzy models rules were written by Mamdani's fuzzy inference method. Then, the outputs of model defuzzified by centroid method. Once again, generation of membership functions and fuzzy rules base as well as optimization of fuzzy rule bases was performed by genetic algorithm, and the fuzzy functions were determined by optimized weight of membership functions and fuzzy rules. Results Discussion: Interrill erodibility parameters (Ki of the examined soils calculated at 3 rainfall rates using are listed in Table 2. The values ranged from 1.03 to 71.79 × 105 kg s m-4, depending on the soil and rainfall intensity. Results showed that the effect of rainfall intensity on Ki turned to be insignificant. This implies that Ki was independent of rainfall intensities. Results showed that the Triangular and Trapezoidal membership functions are better than the other membership functions for linguistic variables which used in this study. The values of R2, RMSE (Root mean square error and GMER (Geometric mean error ratio and GSDER (Geometric standard deviation of error ratio were 0.63, 592755, 1.31 and 1.38 for the fuzzy model, and, 0.70, 441942, 1.10 and 1.044 for the fuzzy- genetic model, respectively. Higher R2 and lower RMSE of the fuzzy – genetic model shows higher accuracy and efficiency of the fuzzy-genetic model. The GSDER criteria shows better matching of the fuzzy- genetic model estimated values with measured values. The GMER criteria shows lower over-estimation of the fuzzy- genetic model than fuzzy model. Conclusion: Fuzzy and fuzzy-genetic models which were designed with two input variables namely aggregates fractal dimensions and soil sand content, capable to predict of interrill erodibility coefficient of soils with reasonable accuracy. So using of these models for predicting of interrill erodibility is recommended.Optimization of fuzzy rule bases