Journal articles: 'Fuzzy c-means clustering analysis'

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Rayala, Venkat, and Satyanarayan Reddy Kalli. "Big Data Clustering Using Improvised Fuzzy C-Means Clustering." Revue d'Intelligence Artificielle 34, no. 6 (December 31, 2020): 701–8. http://dx.doi.org/10.18280/ria.340604.

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Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of it is to categorize the data into clusters where objects are grouped into the particular category. However, there are various challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises the encoder decoder Convolutional Neural Network (CNN) model and Fuzzy C-means (FCM) technique to enhance the clustering mechanism. Encoder decoder based CNN is used for learning feature and faster computation. In general, FCM, we introduce a function which measure the distance between the cluster center and instance which helps in achieving the better clustering and later we introduce Optimized Encoder Decoder (OED) CNN model for improvising the performance and for faster computation. Further in order to evaluate the proposed mechanism, three distinctive data types namely Modified National Institute of Standards and Technology (MNIST), fashion MNIST and United States Postal Service (USPS) are used, also evaluation is carried out by considering the performance metric like Accuracy, Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Moreover, comparative analysis is carried out on each dataset and comparative analysis shows that IFCM outperforms the existing model.

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Hidayat, Syahroni, Ria Rismayati, Muhammad Tajuddin, and Ni Luh Putu Merawati. "Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (March 11, 2020): 133–39. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.133-139.

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One of the strategic plans of the developing universities in obtaining new students is forming a partnership with surrounding high schools. However, partnerships made does not always behave as expected. This paper presented the segmentation technique to the previous new student admission dataset using the integration of recency, frequency, and monetary (RFM) analysis and fuzzy c-means (FCM) algorithm to evaluate the loyalty of the entire school that has bound the partnership with the institution. The dataset is converted using the RFM approach before processed with the FCM algorithm. The result reveals that the schools can be segmented, respectively, as high potential (SP), potential (P), low potential (CP), and very low potential (KP) categories with PCI value 0.86. From the analysis of SP, P, and CP, only 71 % of 52 school partners categorized as loyal partners.

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A, Dharmarajan, and Velmurugan T. "Performance Analysis on K-Means and Fuzzy C-Means Clustering Algorithms Using CT-DICOM Images of Lung Cancer." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 494–502. http://dx.doi.org/10.5373/jardcs/v11/20192597.

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Khang, Tran Dinh, Nguyen Duc Vuong, Manh-Kien Tran, and Michael Fowler. "Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients." Algorithms 13, no. 7 (June 30, 2020): 158. http://dx.doi.org/10.3390/a13070158.

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Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

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Kunwar, Vineeta, A. Sai Sabitha, Tanupriya Choudhury, and Archit Aggarwal. "Chronic Kidney Disease Using Fuzzy C-Means Clustering Analysis." International Journal of Business Analytics 6, no. 3 (July 2019): 43–64. http://dx.doi.org/10.4018/ijban.2019070104.

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Medical industries are encountered with challenges like providing quality services to patients, correct diagnosis and effective treatments at reasonable cost. Data mining has become a necessity and provides solutions to many important and critical health related concerns. It is the process to mine knowledgeable information from voluminous medical data sets. It plays an essential role in improving medical decision making and helps to investigate trends in patient conditions which can be used by doctors for disease diagnosis. Clustering is an unsupervised learning technique that groups object with high similarity together. Chronic kidney disease (CKD) causes renal failure and kidney dysfunction. It has become an important health issue with the number of cases on the rise every year. This article presents analysis and detection of Chronic Kidney Disease using Fuzzy C Means (FCM) clustering which is effective in mining complex data having fuzzy relationships among members. FCM will investigate and group together the patients having CKD and Not CKD. The simulation and coding are done in MATLAB.

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Rosadi, R., Akamal, R. Sudrajat, B. Kharismawan, and Y. A. Hambali. "Student academic performance analysis using fuzzy C-means clustering." IOP Conference Series: Materials Science and Engineering 166 (January 2017): 012036. http://dx.doi.org/10.1088/1757-899x/166/1/012036.

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Hu, Qiongqiong, Yiyang Li, Yong Ge, Yu-an Zhang, Qinglian Ma, and Makoto Sakamoto. "Clustering Analysis Based on Improved Fuzzy C - Means Algorithm." Proceedings of International Conference on Artificial Life and Robotics 23 (February 2, 2018): 276–81. http://dx.doi.org/10.5954/icarob.2018.os1-5.

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Wan, Shuting, and Xiong Zhang. "Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means." Structural Health Monitoring 19, no. 6 (April 14, 2020): 1976–88. http://dx.doi.org/10.1177/1475921720910710.

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Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feature vectors. Then, the mean-shift method is used to search for the high probability density region of principal components so as to determine the cluster number and cluster center. Finally, fuzzy C-means is used to update the clustering center and membership value, and confirm the optimal clustering center and the type of clustering. Through simulated and experimental analysis, the proposed method has two advantages. The feature vector constructed by this method has better specificity than wavelet energy entropy. The initial clustering center of fuzzy C-means is confirmed by the mean-shift method, which can improve the clustering performance of fuzzy C-means and solve the misclassification without preknowing the number of categories.

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Sahu, Sanat Kumar, and A. K. Shrivas. "Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm." International Journal of Computer Vision and Image Processing 8, no. 4 (October 2018): 16–25. http://dx.doi.org/10.4018/ijcvip.2018100102.

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The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical clustering was the lowest at 92. 25%. After using evolutionary technique Genetic Algorithm as Feature selection technique, the best purity is obtained by hierarchical clustering, 97.50%, compared to K –means clustering, 96.75%, and Fuzzy C-means clustering at 94.00%.

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Komori, Osamu, and Shinto Eguchi. "A Unified Formulation of k-Means, Fuzzy c-Means and Gaussian Mixture Model by the Kolmogorov–Nagumo Average." Entropy 23, no. 5 (April 24, 2021): 518. http://dx.doi.org/10.3390/e23050518.

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Clustering is a major unsupervised learning algorithm and is widely applied in data mining and statistical data analyses. Typical examples include k-means, fuzzy c-means, and Gaussian mixture models, which are categorized into hard, soft, and model-based clusterings, respectively. We propose a new clustering, called Pareto clustering, based on the Kolmogorov–Nagumo average, which is defined by a survival function of the Pareto distribution. The proposed algorithm incorporates all the aforementioned clusterings plus maximum-entropy clustering. We introduce a probabilistic framework for the proposed method, in which the underlying distribution to give consistency is discussed. We build the minorize-maximization algorithm to estimate the parameters in Pareto clustering. We compare the performance with existing methods in simulation studies and in benchmark dataset analyses to demonstrate its highly practical utilities.

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.Chitraa, V., and Antony Selvadoss Thanamani. "Web Log Data Analysis by Enhanced Fuzzy C Means Clustering." International Journal on Computational Science & Applications 4, no. 2 (April 30, 2014): 81–95. http://dx.doi.org/10.5121/ijcsa.2014.4209.

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Mansfield, James R., Michael G. Sowa, Gordon B. Scarth, Rajmund L. Somorjai, and Henry H. Mantsch. "Analysis of Spectroscopic Imaging Data by Fuzzy C-Means Clustering." Analytical Chemistry 69, no. 16 (August 1997): 3370–74. http://dx.doi.org/10.1021/ac970206r.

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Scaria, Thomas, Gifty Stephen, and Juby Mathew. "Gene Expression Data Analysis using Fuzzy C-means Clustering Technique." International Journal of Computer Applications 135, no. 8 (February 17, 2016): 33–36. http://dx.doi.org/10.5120/ijca2016908470.

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Chen, Jiashun, Hao Zhang, Dechang Pi, Mehmed Kantardzic, Qi Yin, and Xin Liu. "A Weight Possibilistic Fuzzy C-Means Clustering Algorithm." Scientific Programming 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/9965813.

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Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X12. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.

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Nurfaizah, Nurfaizah, and Fathuzaen Fathuzaen. "Clustering Customer Data Using Fuzzy C-Means Algorithm." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 9, no. 1 (March 24, 2021): 1–14. http://dx.doi.org/10.33558/piksel.v9i1.2359.

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The pattern of the service industry is influenced mostly by economic growth. When economic growth rises, the economic activity will also grow as in the case of insurance activities. One of the assets owned by an insurance company is the customer, hence the existence of a loyal or potential customer should be maintained by the insurance company. This study focuses on clustering or grouping the existing customer data in insurance companies using the Fuzzy C-Means (FCM) algorithm. This study uses data from the company for analysis and the results can be used as a basis for insurance companies in making decisions, especially those related to further insurance marketing to customers who have participated in insurance or who are still actively registered in payment insurance. Fuzzy C-Means can be used for clustering the customer datasets. It obtained 3 clustering results using Partition Coefficient (PC) in determining the validity index and the centers value was ranged from 0.5 to 1.0.

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Yan, Ming Xia. "Drilling Wear Recognition Based on Fuzzy C-Means Clustering Algorithm." Advanced Materials Research 538-541 (June 2012): 1408–12. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.1408.

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Fuzzy c-means clustering algorithm was introduced in detail to classify a set of original sampling data on drilling wear in this paper. Simulation results by Matlab programming show that drill wear modes can be successfully represented by four fuzzy grades after fuzzy clustering and classification. The analysis result indicates that fuzzy description can properly reflect drill wear, FCM can effectively identify different wear modes. It is suggested that the severe degree of membership of wear be used as a criterion for replacement of a drill. This technique is simple and is adaptable to different environment in automatic manufacturing

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Han, Se-Jin, Jae-Beom Myoung, Jae Sakong, Sung-Choong Woo, and Tae-Won Kim. "Analysis of Wound Evidence and Prediction of Threat Shape Based on the Fuzzy C-Means Clustering." Transactions of the Korean Society of Mechanical Engineers - A 43, no. 12 (December 31, 2019): 891–901. http://dx.doi.org/10.3795/ksme-a.2019.43.12.891.

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Han, Lixin, Xiaoqin Zeng, and Hong Yan. "Fuzzy clustering analysis of microarray data." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 222, no. 7 (October 1, 2008): 1143–48. http://dx.doi.org/10.1243/09544119jeim384.

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Fuzzy clustering is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy clustering method for microarray data analysis. An advantage of the method is that it used a combination of the fuzzy c-means and the principal component analysis to identify the groups of genes that show similar expression patterns. It allows a gene to belong to more than a gene expression pattern with different membership grades. The method is suitable for the analysis of large amounts of noisy microarray data.

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Zhang, Wenyuan, Tianyu Huang, and Jun Chen. "A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm." Mathematical Problems in Engineering 2019 (June 18, 2019): 1–17. http://dx.doi.org/10.1155/2019/5984649.

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This paper proposes a modified fuzzy C-means (FCM) algorithm, which combines the local spatial information and the typicality of pixel data in a new fuzzy way. This new algorithm is called bias-correction fuzzy weighted C-ordered-means (BFWCOM) clustering algorithm. It can overcome the shortcomings of the existing FCM algorithm and improve clustering performance. The primary task of BFWCOM is the use of fuzzy local similarity measures (space and grayscale). Meanwhile, this new algorithm adds a typical analysis of data attributes to membership, in order to ensure noise insensitivity and the preservation of image details. Secondly, the local convergence of the proposed algorithm is mathematically proved, providing a theoretical preparation for fuzzy classification. Finally, data classification and real image experiments show the effectiveness of BFWCOM clustering algorithm, having a strong denoising and robust effect on noise images.

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Li, Xiang, Xin Lu, Jing Tian, Peng Gao, Hongwei Kong, and Guowang Xu. "Application of Fuzzy c-Means Clustering in Data Analysis of Metabolomics." Analytical Chemistry 81, no. 11 (June 2009): 4468–75. http://dx.doi.org/10.1021/ac900353t.

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Pandit, Shubhangi, and Rekha Rathore. "An Improved Hierarchical Clustering Using Fuzzy C-Means Clustering Technique for Document Content Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 4 (April 30, 2017): 341–43. http://dx.doi.org/10.23956/ijarcsse/v7i4/0193.

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Xie, Guo, Minying Ye, Xinhong Hei, Fucai Qian, Han Liu, Ding Liu, Bangcheng Sun, and Junbin Mu. "2P15 An analysis of High-Speed Train Axle Temperature Based on Fuzzy C-Means Clustering Algorithm(Shotgun Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _2P15–1_—_2P15–7_. http://dx.doi.org/10.1299/jsmestech.2015._2p15-1_.

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Mohammed, Bakhtyar Ahmed, and Muzhir Shaban Al-Ani. "Digital Medical Image Segmentation Using Fuzzy C-Means Clustering." UHD Journal of Science and Technology 4, no. 1 (February 27, 2020): 51. http://dx.doi.org/10.21928/uhdjst.v4n1y2020.pp51-58.

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In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

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SUN, XICHEN, QIANSHENG CHENG, and JUFU FENG. "FROM PENALIZED MAXIMUM LIKELIHOOD TO CLUSTER ANALYSIS: A UNIFIED PROBABILISTIC FRAMEWORK OF CLUSTERING." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 03 (May 2007): 483–90. http://dx.doi.org/10.1142/s0218001407005569.

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A unified probabilistic framework (UPF) of partitional clustering algorithms is proposed based on Penalized Maximum Likelihood. Besides Gaussian Mixture model methods, many popular clustering methods, such as Fuzzy c-Means Algorithm (FCM), Attribute Means Clustering (AMC), General c-Means Clustering (GCM), and Deterministic Annealing (DA) Clustering can be explained as special cases within UPF. Furthermore, this UPF framework provides a general approach to design comparatively stable and effectively regularized clustering algorithms.

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Huang, Cheng Quan. "A Modified Fuzzy C-Mean Algorithm for Automatic Clustering Number." Applied Mechanics and Materials 333-335 (July 2013): 1418–21. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1418.

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FCM(Fuzzy C-Means) algorithm is an important algorithm in cluster analysis. It plays an significant role in theory and practice. However, the clustering number of FCM algorithm needs to be set beforehand. This paper proposes an automatic clustering number determination for the classical FCM(Fuzzy C-Means) algorithm. The proposed automatic clustering number determination is based on the cardinality of clustering fuzzy membership used in the CA(Competitive Agglomeration) algorithm. The effectiveness of the proposed algorithm, along with a comparison with CA algorithm, has been showed both qualitatively and quantitatively on a set of real-life datasets.

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Li, Enwen, Linong Wang, Bin Song, and Siliang Jian. "Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data." Energies 11, no. 9 (September 5, 2018): 2344. http://dx.doi.org/10.3390/en11092344.

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Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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Cao, Yu Xiang, Xue Jun Li, and Ling Li Jiang. "Fault Diagnosis of Motor Rotor Based on Fuzzy C-Means Clustering Analysis." Applied Mechanics and Materials 273 (January 2013): 409–13. http://dx.doi.org/10.4028/www.scientific.net/amm.273.409.

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For the fuzziness of the fault symptoms in motor rotor, this paper proposes a fault diagnostic method which based on the time-domain statistical features and the fuzzy c-means clustering analysis (FCM). This method is to extract the characteristic features of time-domain signal via time-domain statistics and to import the extracted characteristic vector to classifier. And then the fuzzy c-means realizes the classification by confirming the distance among samples, which is based on the degree of membership between the sample and the clustering center. The fault diagnostic cases of motor rotor show that the method which bases on the time-domain statistical features-FCM can detect the rotor fault effectively and distinguish the different types of fault correctly. Therefore, it can be used as an important means of rotor fault identification.

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Hou, Li Bo. "Improved Fuzzy FCM-LI Algorithm." Advanced Materials Research 765-767 (September 2013): 670–73. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.670.

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Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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El Harchaoui, Nour-Eddine, Mounir Ait Kerroum, Ahmed Hammouch, Mohamed Ouadou, and Driss Aboutajdine. "Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI." Computational Intelligence and Neuroscience 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/435497.

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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.

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Văleanu, A., D. Margină, D. Grădinaru, and M. Ilie. "A fuzzy c-means and k-means clustering analysis on relevant diabetic retinopathy biomarkers." Toxicology Letters 258 (September 2016): S117. http://dx.doi.org/10.1016/j.toxlet.2016.06.1476.

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Chowdhary, Chiranji Lal, and D. P. Acharjya. "Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images." Journal of Biomimetics, Biomaterials and Biomedical Engineering 30 (January 2017): 12–23. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.30.12.

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Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.

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Chen, Benhui, Jinglu Hu, Lihua Duan, and Yinglong Gu. "Network Administrator Assistance System Based on Fuzzy C-means Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 2 (March 20, 2009): 91–96. http://dx.doi.org/10.20965/jaciii.2009.p0091.

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In this research we design a network administrator assistance system based on traffic measurement and fuzzy c-means (FCM) clustering analysis method. Network traffic measurement is an essential tool for monitoring and controlling communication network. It can provide valuable information about network traffic-load patterns and performances. The proposed system utilizes the FCM method to analyze users' network behaviors and traffic-load patterns based on traffic measurement data of IP network. Analysis results can be used as assistance for administrator to determine efficient controlling and configuring parameters of network management systems. The system is applied in Dali University campus network, and it is effective in practice.

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Bezdek, James C. "Generalized C-Means Algorithms for Medical Image Analysis." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 1 (August 12, 1990): 448–49. http://dx.doi.org/10.1017/s0424820100180999.

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Diagnostic machine vision systems that attempt to interpret medical imagery almost always include (and depend upon) one or more pattern recognition algorithms (cluster analysis and classifier design) for low and intermediate level image data processing. This includes, of course, image data collected by electron microscopes. Approaches based on both statistical and fuzzy models are found in the texts by Bezdek, Duda and Hart, Dubes and Jain,and Pao. Our talk examines the c-means families as they relate to medical image processing. We discuss and exemplify applications in segmentation (MRI data); clustering (flow cytometry data); and boundary analysis.The structure of partition spaces underlying clustering algorithms is described briefly. Let (c) be an integer, 1<c>n and let X = {x1, x2, ..., xn} denote a set of (n) column vectors in Rs. X is numerical object data; the k-th object (some physical entity such as a medical patient, PAP smear image, color photograph, etc.) has xk as its numerical representation; xkj is the j-th characteristic (or feature) associated with object k.

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Khang, Tran Dinh, Manh-Kien Tran, and Michael Fowler. "A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients." Algorithms 14, no. 9 (August 29, 2021): 258. http://dx.doi.org/10.3390/a14090258.

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Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.

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Benzian, Yaghmorasan, and Nacéra Benamrane. "New FCM Segmentation Approach Based on Multi-Resolution Analysis." International Journal of Fuzzy System Applications 7, no. 4 (October 2018): 100–114. http://dx.doi.org/10.4018/ijfsa.2018100105.

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This article presents a modified Fuzzy C Means segmentation approach based on multi-resolution image analysis. Fuzzy C-Means standard methods are improved through fuzzy clustering at different image resolution levels by propagating fuzzy membership values pyramidally from a lower to a higher level. Processing at a lower resolution image level provides a rough pixel classification result, thus, a pixel is assigned to a cluster to which the majority of its neighborhood pixels belongs. The aim of fuzzy clustering with multi-resolution images is to avoid pixel misclassification according to the spatial cluster of the neighbourhood of each pixel in order to have more homogeneous regions and eliminate noisy regions present in the image. This method is tested particularly on samples and medical images with gaussian noise by varying multiresolution parameter values for better analysis. The results obtained after multi-resolution clustering are giving satisfactory results by comparing this approach with standard FCM and spatial FCM ones.

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Liu, Shing-Hong, Kang-Ming Chang, and Chu-Chang Tyan. "FUZZY C-MEANS CLUSTERING FOR MYOCARDIAL ISCHEMIA ESTIMATION WITH PULSE WAVEFORM ANALYSIS." Biomedical Engineering: Applications, Basis and Communications 21, no. 02 (April 2009): 139–47. http://dx.doi.org/10.4015/s1016237209001143.

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The purpose of this study is to build an automatic disease classification algorithm by pulse waveform analysis, based on a Fuzzy C-means clustering algorithm. A self designed three-axis mechanism was used to detect the optimal position to accurately measure the pressure pulse waveform (PPW). Considering the artery as a cylinder, the sensor should detect the PPW with the lowest possible distortion, and hence an analysis of the vascular geometry and an arterial model were used to design a standard positioning procedure based on the arterial diameter changed waveform for the X-axes (perpendicular to the forearm) and Z-axes (perpendicular to the radial artery). A fuzzy C-means algorithm was used to estimate the myocardial ischemia symptoms in 35 elderly subjects with the PPW of the radial artery. Two type parameters were used to make the features, one was a harmonic value of Fourier transfer, and the other was a form factor value. A receiver operating characteristics curve was used to determine the optimal decision function. The harmonic feature vector contain second, third and fourth harmonics ( H 2, H 3, H 4) performed at the level of 69% for sensitivity and 100% for specificity while the form factor feature vector derived from left hand (LFF) and right hand (RFF) performed at the level of 100% for sensitivity and 53% for specificity. The FCM- and ROC-based clustering approach may become an efficient alternative for distinguishing patients in the risk of myocardial ischemia, besides the traditional exercise ECG examination.

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Singh, Neha, Agrawal Kalpesh, and Swathi Narayanan. "Fault Prone Analysis of Software Systems Using Rough Fuzzy C- means Clustering." International Journal of Intelligent Engineering and Systems 10, no. 6 (December 31, 2017): 1–8. http://dx.doi.org/10.22266/ijies2017.1231.01.

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Di Martino, Ferdinando, Vincenzo Loia, and Salvatore Sessa. "Extended fuzzy C-means clustering algorithm for hotspot events in spatial analysis." International Journal of Hybrid Intelligent Systems 5, no. 1 (April 23, 2008): 31–44. http://dx.doi.org/10.3233/his-2008-5103.

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Choudhry, Mahipal Singh, and Rajiv Kapoor. "Performance Analysis of Fuzzy C-Means Clustering Methods for MRI Image Segmentation." Procedia Computer Science 89 (2016): 749–58. http://dx.doi.org/10.1016/j.procs.2016.06.052.

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Lim, C. P., and W. S. Ooi. "An empirical analysis of colour image segmentation using fuzzy c-means clustering." International Journal of Knowledge Engineering and Soft Data Paradigms 2, no. 1 (2010): 97. http://dx.doi.org/10.1504/ijkesdp.2010.030469.

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Natarajan, Thamaraiselvan, Sridevi Periaiya, Senthil Arasu Balasubramaniam, and Thushara Srinivasan. "Identification and analysis of employee branding typology using fuzzy c-means clustering." Benchmarking: An International Journal 24, no. 5 (July 3, 2017): 1253–68. http://dx.doi.org/10.1108/bij-01-2016-0010.

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Purpose The purpose of this paper is to identify and analyse the typology of employee branding in an airline company using fuzzy c-means (FCM) clustering to improve the quality of employee brand (EB). Design/methodology/approach Data were collected from employees of Air India, Chennai division, using a questionnaire and analysed using FCM to find the optimum cluster number. The nature of each cluster was analysed to know its type. Findings The results prove the presence of four types of EB, namely, all-stars, injured reserves, rookies and strike-out kings in the aviation company. It is proven that employees in all-star have high level of knowledge of the desired brand (KDB) and psychological contract (PC), those in injured reserves have high KDB and low PC, rookies have low KDB and high PC and strike-out kings have low KDB and PC. Research limitations/implications The results of this study are limited to the Air India employees. This study contributes to employee branding by empirically substantiating the proposed typology using FCM. It proposes the need to analyse organisations individually before comparisons. Practical implications The management must focus on the quality of training and development programmes to enhance the position of rookies and strike-out kings. It must also receive regular feedback from injured reserves and strike-out kings to evaluate their perception of PC. Originality/value This is the first paper to empirically prove the typology of employee branding and to implement FCM in clustering employees for enhancing the EB’s quality.

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Al Kindhi, Berlian, Tri Arief Sardjono, Mauridhi Hery Purnomo, and Gijbertus Jacob Verkerke. "Hybrid K-means, fuzzy C-means, and hierarchical clustering for DNA hepatitis C virus trend mutation analysis." Expert Systems with Applications 121 (May 2019): 373–81. http://dx.doi.org/10.1016/j.eswa.2018.12.019.

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Li, Hong Fei, Fu Ling Wang, Shi Jue Zheng, and Li Gao. "An Improved Fuzzy C-Means Clustering Algorithm and Application in Meteorological Data." Advanced Materials Research 181-182 (January 2011): 545–50. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.545.

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The fuzzy clustering algorithm is sensitive to the m value and the degree of membership. Because of the deficiencies of traditional FCM clustering algorithm, we made specific improvement. Through the calculation of the value of m, the amendments of degree of membership to the discussion of issues, effectively compensate for the deficiencies of the traditional algorithm and achieve a relatively good clustering effect. Finally, through the analysis of temperature observation data of the three northeastern province of china in 2000, the reasonableness of the method is verified.

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Ansari, Mohd Yousuf, Anand Prakash, and Dr Mainuddin. "Application of Spatiotemporal Fuzzy C-Means Clustering for Crime Spot Detection." Defence Science Journal 68, no. 4 (June 26, 2018): 374. http://dx.doi.org/10.14429/dsj.68.12518.

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<p>The various sources generate large volume of spatiotemporal data of different types including crime events. In order to detect crime spot and predict future events, their analysis is important. Crime events are spatiotemporal in nature; therefore a distance function is defined for spatiotemporal events and is used in Fuzzy C-Means algorithm for crime analysis. This distance function takes care of both spatial and temporal components of spatiotemporal data. We adopt sum of squared error (SSE) approach and Dunn index to measure the quality of clusters. We also perform the experimentation on real world crime data to identify spatiotemporal crime clusters.</p><div> </div>

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Kyu Lee, Kwang, and . "A Comparative Study between of Fuzzy C-Means Algorithms and Density based Spatial Clustering of Applications with Noise." International Journal of Engineering & Technology 7, no. 3.33 (August 29, 2018): 131. http://dx.doi.org/10.14419/ijet.v7i3.33.18592.

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Data mining technology has emerged as a means of identifying patterns and trends from large amounts of data and is a computing intelligence area that provides tools for data analysis, new knowledge discovery, and autonomous decision making. Data clustering is an important problem in many areas. Fuzzy C-Means(FCM)[11,12,13] is a very important clustering technique based on fuzzy logic. DBSCAN(Density Based Spatial Clustering of Applications with Noise)[8] is a density-based clustering algorithm that is suitable for dealing with spatial data including noise and is a collection of arbitrary shapes and sizes. In this paper, we compare and analyze the performance of Fuzzy C-Means and DBSCAN algorithms in different data sets.

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Sanusi, Wahidah, Ahmad Zaky, and Besse Nur Afni. "Analisis Fuzzy C-Means dan Penerapannya Dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Faktor-faktor Penyebab Gizi Buruk." Journal of Mathematics, Computations, and Statistics 2, no. 1 (May 12, 2020): 47. http://dx.doi.org/10.35580/jmathcos.v2i1.12458.

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Dalam analisis pengeompokan (cluster), banyak kelompok menjadi suatu masalah yang berarti. Beberapa peneliti memiliki banyak kelompok sesuai dengan kebutuhan dalam penelitiannya. FCM melakukan pengelompokan dengan prinsip meminimumkan fungsi pengelompokannya dimana salah satu parameternya adalah fungsi keanggotaan dalam fuzzy (sebagai pembobot) yang disebut juga dengan fuzzier. Penelitian ini bertujuan untuk mengkaji metode pengelompokan dengan Fuzzy C-Means Clustering dan penerapannya dalam pengelompokan Kabupaten/Kota di Sulawesi Selatan berdasarkan Faktor-faktor Penyebab Gizi Buruk yakni sarana dan tenaga kesehatan, kependudukan, perekonomian yang rendah, serta asupan gizi yang rendah. Dari hasil analisis pengelompokan Fuzzy C-Means dengan 2 cluster diperoleh fungsi objektif sebesar 1079141921,2224. Dimana kelompok pertama terdiri dari 18 kabupaten/kota sedangkan kelompok kedua terdiri atas6 kabupaten.Kata Kunci:Cluster, Fuzzy-C-Means, Fuzzier In the analysis of clustering, many groups became an issue. Some researchers chose many groups that match the needs of their research. FCM performs grouping with the principle of minimising its categorization function where one of the parameters is a membership function in fuzzy (as weighing), also known as with fuzzier .This research aimed to study the methods of grouping with Fuzzy C-Means Clustering and its application in the classification of grouping at Regency/City of South Sulawesi based on factors of Causes of Malnutrition i.e. in terms of facilities and health workers, population, economy, and low nutrient intake that is low. From the results of the analysis of the classification with Fuzzy C-Means with 2 clusters with the objective function respectively is 1079141921.2224. When the first group of 18 district while the second group consists of 6 counties.Keywords:Cluster, Fuzzy C-Meanas, Fuzzier

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Rajkumar, K. Varada, Adimulam Yesubabu, and K. Subrahmanyam. "Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2760. http://dx.doi.org/10.11591/ijece.v9i4.pp2760-2770.

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A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.

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Harikiran, J., P. V. Lakshmi, and R. Kiran Kumar. "Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (October 1, 2015): 1045. http://dx.doi.org/10.11591/ijece.v5i5.pp1045-1053.

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<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. Clustering algorithms have been used for microarray image segmentation with an advantage that they are not restricted to a particular shape and size for the spots. Instead of using single feature clustering algorithm, this paper presents multiple feature clustering algorithm with three features for each pixel such as pixel intensity, distance from the center of the spot and median of surrounding pixels. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user. In this paper, a new algorithm based on empirical mode decomposition algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering. It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that multiple feature Fuzzy C-means has segmented the microarray image more accurately than other algorithms.</p>

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D Lalitha Bhaskari, P. Gopala Krishna,. "AN EFFICIENTFUZZY C-MEANS CLUSTERING ALGORITHM FOR MULTI-VALUED DATA SETS." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 18, 2021): 1250–64. http://dx.doi.org/10.17762/itii.v9i1.265.

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In data analysis, items were mostly described by a set of characteristics called features, in which each feature contains only single value for each object. Even so, in existence, some features may include more than one value, such as a person with different job descriptions, activities, phone numbers, skills and different mailing addresses. Such features may be called as multi-valued features, and are mostly classified as null features while analyzing the data using machine learning and data mining techniques. In this paper, it is proposed a proximity function to be described between two substances with multi-valued features that are put into effect for clustering.The suggested distance approach allows iterative measurements of the similarities around objects as well as their characteristics. For facilitating the most suitable multi-valued factors, we put forward a model targeting at determining each factor’s relative prominence for diverse data extracting problems. The proposed algorithm is a partition clustering strategy that uses fuzzy c- means clustering for evolutions, which is using the novel member ship function by utilizing the proposed similarity measure. The proposed clustering algorithm as fuzzy c- means based Clustering of Multivalued Attribute Data (FCM-MVA).Therefore this becomes feasible using any mechanisms for cluster analysis to group similar data. The findings demonstrate that our test not only improves the performance the traditional measure of similarity but also outperforms other clustering algorithms on the multi-valued clustering framework.

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Chen, Yao-Tien. "Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy c-Means Clustering." Mathematical Problems in Engineering 2017 (2017): 1–21. http://dx.doi.org/10.1155/2017/5892039.

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Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one. The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images. The proposed method first removes the skull region using a skull stripping algorithm. Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images. As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images. Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues. Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches. According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.

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Journal articles on the topic 'Fuzzy c-means clustering analysis'

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