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Journal articles on the topic 'Possibilistic clustering'

Author: Grafiati

Published: 28 September 2022

Last updated: 25 July 2025

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1

Miyamoto, Sadaaki, Youhei Kuroda, and Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

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In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.

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2

Ubukata, Seiki, Katsuya Koike, Akira Notsu, and Katsuhiro Honda. "MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (2018): 747–58. http://dx.doi.org/10.20965/jaciii.2018.p0747.

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In the field of cluster analysis, fuzzy theory including the concept of fuzzy sets has been actively utilized to realize flexible and robust clustering methods. FuzzyC-means (FCM), which is the most representative fuzzy clustering method, has been extended to achieve more robust clustering. For example, noise FCM (NFCM) performs noise rejection by introducing a noise cluster that absorbs noise objects and possibilisticC-means (PCM) performs the independent extraction of possibilistic clusters by introducing cluster-wise noise clusters. Similarly, in the field of co-clustering, fuzzy co-cluster

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3

Yang, Miin-Shen, and Kuo-Lung Wu. "Unsupervised possibilistic clustering." Pattern Recognition 39, no. 1 (2006): 5–21. http://dx.doi.org/10.1016/j.patcog.2005.07.005.

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ZHOU, JIAN, and CHIH-CHENG HUNG. "A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, supp02 (2007): 117–38. http://dx.doi.org/10.1142/s0218488507004650.

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Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theor

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Pimentel, Bruno Almeida, and Renata M. C. R. de Souza. "A Generalized Multivariate Approach for Possibilistic Fuzzy C-Means Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 06 (2018): 893–916. http://dx.doi.org/10.1142/s021848851850040x.

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Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) are the most popular algorithms of the fuzzy and possibilistic clustering approaches, respectively. A hybridization of these methods, called Possibilistic Fuzzy c-Means (PFCM), solves noise sensitivity defect of FCM and overcomes the coincident clusters problem of PCM. Although PFCM have shown good performance in cluster detection, it does not consider that different variables can produce different membership and possibility degrees and this can improve the clustering quality as it has been performed with the Multivariate Fuzzy c-Means (MFCM)

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De Cáceres, Miquel, Francesc Oliva, and Xavier Font. "On relational possibilistic clustering." Pattern Recognition 39, no. 11 (2006): 2010–24. http://dx.doi.org/10.1016/j.patcog.2006.04.008.

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7

Treerattanapitak, Kiatichai, and Chuleerat Jaruskulchai. "Possibilistic Exponential Fuzzy Clustering." Journal of Computer Science and Technology 28, no. 2 (2013): 311–21. http://dx.doi.org/10.1007/s11390-013-1331-7.

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Naghi, Mirtill-Boglárka, Levente Kovács, and László Szilágyi. "A generalized fuzzy-possibilistic c-means clustering algorithm." Acta Universitatis Sapientiae, Informatica 15, no. 2 (2023): 404–31. http://dx.doi.org/10.2478/ausi-2023-0023.

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Abstract The so-called fuzzy-possibilistic c-means (FPCM) algorithm was introduced as an early mixed-partition method aiming to eliminate some adverse effects present in the behavior of the fuzzy c-means (FCM) and the possibilistic c-means (PCM) algorithms. A great advantage of FPCM was the low number of its parameters, as it eliminated the possibilistic penalty terms used by PCM. Unfortunately, FPCM in its original formulation also has a weak point: the strength of the possibilistic term is in inverse proportion with the number of clustered data items, which makes FPCM act like FCM when clust

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9

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 ma

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10

Hamasuna, Yukihiro, and Yasunori Endo. "On Sequential Cluster Extraction Based onL1-Regularized Possibilisticc-Means." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 5 (2015): 655–61. http://dx.doi.org/10.20965/jaciii.2015.p0655.

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Sequential cluster extraction algorithms are useful clustering methods that extract clusters one by one without the number of clusters having to be determined in advance. Typical examples of these algorithms are sequential hardc-means (SHCM) and possibilistic clustering (PCM) based algorithms. Two types ofL1-regularized possibilistic clustering are proposed to induce crisp and possibilistic allocation rules and to construct a novel sequential cluster extraction algorithm. The relationship between the proposed method and SHCM is also discussed. The effectiveness of the proposed method is verifi

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Wu, Xiao Hong, Tong Xiang Cai, Bin Wu, and Jun Sun. "Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering." Advanced Materials Research 710 (June 2013): 768–71. http://dx.doi.org/10.4028/www.scientific.net/amr.710.768.

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Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also t

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Esaki, Tomohito, Tomonori Hashiyama, and Yahachiro Tsukamoto. "Fuzzy Clustering Based on Total Uncertainty Degree." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 8 (2007): 897–904. http://dx.doi.org/10.20965/jaciii.2007.p0897.

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Traditional Fuzzy c-Means (FCM) methods have probabilistic and additive restrictions that ∑ μ (x) = 1; the sum of membership values on the identified membership function is one. Possibilistic clustering methods identify membership functions without such constraints, but some parameters used in objective functions are difficult to understand and membership function shapes are independent of clusters estimated through possibilistic methods. We propose novel fuzzy clustering using a total uncertainty degree based on evidential theory with which we obtain nonadditive membership functions whose the

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Aghyari, Ghia Fauziah, and Abdul Kudus. "Penerapan Algoritma Fuzzy Possibilistic C-Means (FPCM) pada Pengelompokan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2022." Bandung Conference Series: Statistics 3, no. 2 (2023): 113–20. http://dx.doi.org/10.29313/bcss.v3i2.7321.

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Abstract. Human resources are a crucial factor in human development and a key component in achieving prosperity in every country. The success of development is measured in various ways, one of the most popular being the calculation of the Human Development Index (HDI). The classification of districts and cities in Indonesia is necessary as a reference for government program planning and evaluation to enhance human development in those areas. Partitioning clustering is one of the clustering techniques that aims to partition data into several groups or partitions, with the number of groups usual

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14

Krishnapuram, R., and J. M. Keller. "A possibilistic approach to clustering." IEEE Transactions on Fuzzy Systems 1, no. 2 (1993): 98–110. http://dx.doi.org/10.1109/91.227387.

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15

Xenaki, Spyridoula D., Konstantinos D. Koutroumbas, and Athanasios A. Rontogiannis. "Sparsity-Aware Possibilistic Clustering Algorithms." IEEE Transactions on Fuzzy Systems 24, no. 6 (2016): 1611–26. http://dx.doi.org/10.1109/tfuzz.2016.2543752.

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16

Wan, Renxia, Yuelin Gao, and Caixia Li. "Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets." International Journal of Data Warehousing and Mining 8, no. 4 (2012): 82–107. http://dx.doi.org/10.4018/jdwm.2012100104.

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Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that

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17

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 coinc

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18

Putri, Ghina Nabila Saputro, Dwi Ispriyanti, and Tatik Widiharih. "IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA." Jurnal Gaussian 11, no. 1 (2022): 86–98. http://dx.doi.org/10.14710/j.gauss.v11i1.33996.

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Social media has become the most popular media, which can be accessed by young to old age. Twitter became one of the effective media and the familiar one used by the public, thus making the company make Twitter one of the promotional tools, one of which is Tokopedia. The research aims to group tweets uploaded by @tokopedia Twitter accounts based on the type of tweets content that gets a lot of retweets and likes by followers of @tokopedia. Application of text mining to cluster tweets on the @tokopedia Twitter account using Fuzzy C-Means and Fuzzy Possibilistic C-Means algorithms that viewed th

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19

Hai-Jun, Fu, Wu Xiao-Hong, Mao Han-Ping, and Wu Bin. "Fuzzy Entropy Clustering Using Possibilistic Approach." Procedia Engineering 15 (2011): 1993–97. http://dx.doi.org/10.1016/j.proeng.2011.08.372.

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20

Yu, Haiyan, Jiulun Fan, and Rong Lan. "Suppressed possibilistic c-means clustering algorithm." Applied Soft Computing 80 (July 2019): 845–72. http://dx.doi.org/10.1016/j.asoc.2019.02.027.

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21

Zhang, J. S., and Y. W. Leung. "Improved Possibilistic C-Means Clustering Algorithms." IEEE Transactions on Fuzzy Systems 12, no. 2 (2004): 209–17. http://dx.doi.org/10.1109/tfuzz.2004.825079.

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22

Xenaki, Spyridoula D., Konstantinos D. Koutroumbas, and Athanasios A. Rontogiannis. "A Novel Adaptive Possibilistic Clustering Algorithm." IEEE Transactions on Fuzzy Systems 24, no. 4 (2016): 791–810. http://dx.doi.org/10.1109/tfuzz.2015.2486806.

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23

Szilágyi, László, Szidónia Lefkovits, and Sándor M. Szilágyi. "Self-Tuning Possibilistic c-Means Clustering Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, Supp01 (2019): 143–59. http://dx.doi.org/10.1142/s0218488519400075.

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The relaxation of the probabilistic constraint of the fuzzy c-means clustering model was proposed to provide robust algorithms that are insensitive to strong noise and outlier data. These goals were achieved by the possibilistic c-means (PCM) algorithm, but these advantages came together with a sensitivity to cluster prototype initialization. According to the original recommendations, the probabilistic fuzzy c-means (FCM) algorithm should be applied to establish the cluster initialization and possibilistic penalty terms for PCM. However, when FCM fails to provide valid cluster prototypes due t

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Azzouzi, Souad, Amal Hjouji, Jaouad EL- Mekkaoui, and Ahmed EL Khalfi. "A Generalization of Possibilistic Fuzzy C-Means Method for Statistical Clustering of Data." International Journal of Circuits, Systems and Signal Processing 15 (December 17, 2021): 1766–80. http://dx.doi.org/10.46300/9106.2021.15.191.

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The Fuzzy C-means (FCM) algorithm has been widely used in the field of clustering and classification but has encountered difficulties with noisy data and outliers. Other versions of algorithms related to possibilistic theory have given good results, such as Fuzzy C- Means(FCM), possibilistic C-means (PCM), Fuzzy possibilistic C-means (FPCM) and possibilistic fuzzy C- Means algorithm (PFCM).This last algorithm works effectively in some environments but encountered more shortcomings with noisy databases. To solve this problem, we propose in this manuscript, a new algorithm named Improved Possibi

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Pimentel, Bruno Almeida, and Renata M. C. R. De Souza. "Possibilistic Clustering Methods for Interval-Valued Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 02 (2014): 263–91. http://dx.doi.org/10.1142/s0218488514500135.

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Outliers may have many anomalous causes, for example, credit card fraud, cyberintrusion or breakdown of a system. Several research areas and application domains have investigated this problem. The popular fuzzy c-means algorithm is sensitive to noise and outlying data. In contrast, the possibilistic partitioning methods are used to solve these problems and other ones. The goal of this paper is to introduce cluster algorithms for partitioning a set of symbolic interval-type data using the possibilistic approach. In addition, a new way of measuring the membership value, according to each feature

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Bouzbida, Mohamed, Lassad Hassine, and Abdelkader Chaari. "Robust Kernel Clustering Algorithm for Nonlinear System Identification." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2427309.

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In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM) algorithm, Possibilistic C-Means (PCM) algorithm, and Possibilistic Fuzzy C-Means (PFCM) algorithm. This paper presents a new clustering algo

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Hu, Ya Ting, Fu Heng Qu, Yao Hong Xue, and Yong Yang. "An Efficient and Robust Kernelized Possibilistic C-Means Clustering Algorithm." Advanced Materials Research 962-965 (June 2014): 2881–85. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2881.

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To avoid the initialization sensitivity and low computational efficiency problems of the kernelized possibilistic c-means clustering algorithm (KPCM), a new clustering algorithm called efficient and robust kernelized possibilistic c-means clustering algorithm (ERKPCM) was proposed in this paper. ERKPCM improved KPCM by two ways. First, the data are refined by the data reduction technique, which makes it keep the data structure of the original data and have higher efficiency. Secondly, weighted clustering algorithm is executed several times to estimate cluster centers accurately, which makes it

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Hamasuna, Yukihiro, and Yasunori Endo. "On Cluster Extraction from Relational Data UsingL1-Regularized Possibilistic Assignment Prototype Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 1 (2015): 23–28. http://dx.doi.org/10.20965/jaciii.2015.p0023.

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This paper proposes entropy-basedL1-regularized possibilistic clustering and a method of sequential cluster extraction from relational data.Sequential cluster extractionmeans that the algorithm extracts cluster one by one. The assignment prototype algorithmis a typical clustering method for relational data. The membership degree of each object to each cluster is calculated directly from dissimilarities between objects. An entropy-basedL1-regularized possibilistic assignment prototype algorithm is proposed first to induce belongingness for a membership grade. An algorithm of sequential cluster

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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 Lagrang

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Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On tolerant fuzzy c-means clustering and tolerant possibilistic clustering." Soft Computing 14, no. 5 (2009): 487–94. http://dx.doi.org/10.1007/s00500-009-0451-z.

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31

Gupta, Saroj Kumar, M. V. Jagannatha Reddy, and A. Nanda Kumar. "Possibilistic Clustering Adaptive Smoothing Bilateral Filter Using Artificial Neural Network." International Journal of Engineering and Technology 2, no. 6 (2010): 499–503. http://dx.doi.org/10.7763/ijet.2010.v2.171.

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Jayasree, R., and N. A. Sheela Selvakumari. "Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm." Indian Journal Of Science And Technology 16, no. 38 (2023): 3230–35. http://dx.doi.org/10.17485/ijst/v16i38.226.

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33

Miyamoto, Sadaaki. "Formulation of Fuzzyc-Means Clustering Using Calculus of Variations and Twofold Membership Clusters." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 454–60. http://dx.doi.org/10.20965/jaciii.2008.p0454.

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A membership matrix of fuzzyc-means clustering is associated with corresponding fuzzy classification rules as membership functions defined on the whole data space. We directly derive such functions in fuzzyc-means and possibilistic clustering using the calculus of variations, generalizing ordinary fuzzyc-means and deriving new twofold membership clustering using this framework.

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Yu, Hong, and Hu Luo. "A novel possibilistic fuzzy leader clustering algorithm." International Journal of Hybrid Intelligent Systems 8, no. 1 (2011): 31–40. http://dx.doi.org/10.3233/his-2011-0129.

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Ismail, Mohamed Maher Ben, Sara N. Alfaraj, and Ouiem Bchir. "Automatic Image Annotation using Possibilistic Clustering Algorithm." INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 19, no. 4 (2019): 250–62. http://dx.doi.org/10.5391/ijfis.2019.19.4.250.

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36

Hu, Yating, Chuncheng Zuo, Fuheng Qu, and Weili Shi. "Unsupervised Possibilistic Clustering Based on Kernel Methods." Physics Procedia 25 (2012): 1084–90. http://dx.doi.org/10.1016/j.phpro.2012.03.203.

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37

Chaudhuri, Arindam. "Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms." Advances in Fuzzy Systems 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/238237.

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Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic

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38

Škrjanc, Igor, and Dejan Dovžan. "Evolving Gustafson-kessel Possibilistic c-Means Clustering." Procedia Computer Science 53 (2015): 191–98. http://dx.doi.org/10.1016/j.procs.2015.07.294.

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Tang, Yiming, Xianghui Hu, Witold Pedrycz, and Xiaocheng Song. "Possibilistic fuzzy clustering with high-density viewpoint." Neurocomputing 329 (February 2019): 407–23. http://dx.doi.org/10.1016/j.neucom.2018.11.007.

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Yu, Haiyan, and Jiulun Fan. "Cutset-type possibilistic c-means clustering algorithm." Applied Soft Computing 64 (March 2018): 401–22. http://dx.doi.org/10.1016/j.asoc.2017.12.024.

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Barni, M., V. Cappellini, and A. Mecocci. "Comments on "A possibilistic approach to clustering"." IEEE Transactions on Fuzzy Systems 4, no. 3 (1996): 393–96. http://dx.doi.org/10.1109/91.531780.

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Pal, N. R., K. Pal, J. M. Keller, and J. C. Bezdek. "A possibilistic fuzzy c-means clustering algorithm." IEEE Transactions on Fuzzy Systems 13, no. 4 (2005): 517–30. http://dx.doi.org/10.1109/tfuzz.2004.840099.

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Tsaipei Wang. "Possibilistic Shell Clustering of Template-Based Shapes." IEEE Transactions on Fuzzy Systems 17, no. 4 (2009): 777–93. http://dx.doi.org/10.1109/tfuzz.2008.924360.

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Yang, Miin-Shen, and Chien-Yo Lai. "A Robust Automatic Merging Possibilistic Clustering Method." IEEE Transactions on Fuzzy Systems 19, no. 1 (2011): 26–41. http://dx.doi.org/10.1109/tfuzz.2010.2077640.

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Yang, Miin-Shen, and Josephine B. M. Benjamin. "Sparse possibilistic c-means clustering with Lasso." Pattern Recognition 138 (June 2023): 109348. http://dx.doi.org/10.1016/j.patcog.2023.109348.

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Coppi, Renato, Pierpaolo D’Urso, and Paolo Giordani. "Fuzzy and possibilistic clustering for fuzzy data." Computational Statistics & Data Analysis 56, no. 4 (2012): 915–27. http://dx.doi.org/10.1016/j.csda.2010.09.013.

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Gurler, Orkun. "COMPREHENSIVE ANALYSIS OF FUZZY C - MEANS CLUSTERING AND ITS VARIANTS." Journal of Modern Technology and Engineering 9, no. 1 (2024): 69–93. http://dx.doi.org/10.62476/jmte9269.

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Fuzzy clustering algorithms are one of the most important techniques for analysing and extracting information from data when working with datasets containing overlapping clusters. Fuzzy clustering provides a more precise representation of complex data structures compared to conventional crisp (hard) clustering approaches. It accomplishes this by allowing data points to be assigned to multiple clusters with different degrees of membership. This paper provides an extensive review of various fuzzy clustering algorithms, such as Fuzzy C - Means (FCM) and its variations including Gustafson - Kessel

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Gu, Yuxin, Tongguang Ni, and Yizhang Jiang. "Deep Possibilistic C -means Clustering Algorithm on Medical Datasets." Computational and Mathematical Methods in Medicine 2022 (April 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/3469979.

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In the past, the possibilistic C -means clustering algorithm (PCM) has proven its superiority on various medical datasets by overcoming the unstable clustering effect caused by both the hard division of traditional hard clustering models and the susceptibility of fuzzy C -means clustering algorithm (FCM) to noise. However, with the deep integration and development of the Internet of Things (IoT) as well as big data with the medical field, the width and height of medical datasets are growing bigger and bigger. In the face of high-dimensional and giant complex datasets, it is challenging for the

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Do Viet Duc, Ngo Thanh Long, Ha Trung Hai, Chu Van Hai, and Nghiem Van Tam. "A possibilistic Fuzzy c-means algorithm based on improved Cuckoo search for data clustering." Journal of Military Science and Technology, CSCE6 (December 30, 2022): 3–15. http://dx.doi.org/10.54939/1859-1043.j.mst.csce6.2022.3-15.

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Possibilistic Fuzzy c-means (PFCM) algorithm is a powerful clustering algorithm. It is a combination of two algorithms Fuzzy c-means (FCM) and Possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, PFCM still has a common weakness of clustering algorithms that is easy to fall into local optimization. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be stable and high-efficiency. In this study, we propo

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Wu, Xiao Hong, Bin Wu, and Jie Wen Zhao. "Improved Inter-Cluster Separation Algorithm." Key Engineering Materials 439-440 (June 2010): 361–66. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.361.

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Abstract:

The inter-cluster separation (ICS) algorithm adds the separation item into the objective function to minimize the fuzzy Euclidean distance and maximize the inter-cluster separation. However, ICS is sensitive to noisy data, so an improved inter-cluster separation (IICS) algorithm is proposed to deal with this problem. It is claimed that IICS is an incorporation of ICS and improved possibilistic c-means (IPCM) clustering. IICS can produce both possibilities and memberships simultaneously, and it overcomes the noise sensitivity problem of ICS and the coincident clusters problem of possibilistic c

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