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Автор: Grafiati

Опубліковано: 28 вересня 2022

Оновлено: 25 листопада 2022

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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 (September 20, 2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

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Анотація:

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

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

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3

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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4

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 (November 27, 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). Here, this work presents a generalized multivariate approach for possibilistic fuzzy c-means clustering. This approach gives a general form for the clustering criterion of the possibilistic fuzzy clustering with membership and possibility degrees different by cluster and variable and a weighted squared Euclidean distance in order to take into account the shape of clusters. Six multivariate clustering models (special cases) can be derivative from this general form and their properties are presented. Experiments with real and synthetic data sets validate the usefulness of the approach introduced in this paper using the special cases.

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5

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

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6

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

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7

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

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8

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

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9

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

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Анотація:

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 theory, and the cluster centers are updated via a performance index. This new method is then used to develop a generalized approach of possibilistic clustering to obtain an infinite family of generalized possibilistic clustering algorithms. We also point out that the existing possibilistic clustering algorithms are members of this family. Following that, some specific possibilistic clustering algorithms in the new family are demonstrated by real data experiments, and the results show that these new proposed algorithms are efficient for clustering and easy for computer implementation.

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10

Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On tolerant fuzzy c-means clustering and tolerant possibilistic clustering." Soft Computing 14, no. 5 (June 3, 2009): 487–94. http://dx.doi.org/10.1007/s00500-009-0451-z.

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11

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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12

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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13

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

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14

Xenaki, Spyridoula D., Konstantinos D. Koutroumbas, and Athanasios A. Rontogiannis. "A Novel Adaptive Possibilistic Clustering Algorithm." IEEE Transactions on Fuzzy Systems 24, no. 4 (August 1, 2016): 791–810. http://dx.doi.org/10.1109/tfuzz.2015.2486806.

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15

Yao, Leehter, and Kuei Sung Weng. "Combined Probabilistic and Possibilistic Used to a Build Type-2 Fuzzy Clustering Algorithm Model." Applied Mechanics and Materials 284-287 (January 2013): 3060–69. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3060.

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Анотація:

In a noise environment probabilistic fuzzy clustering will force the noise into one or more clusters, seriously influencing the main dataset structure. We extend Type-1 membership values to Type-2 by assigning a possibilistic-membership function to each Type-1 membership value. The idea in building the Type-2 fuzzy sets is based simply on the fact that, for the same Type-1 membership value, the secondary membership function should make the larger possibility value greater than the smaller possibility value. This paper presents an efficient combined probabilistic and possibilistic method for building Type-2 fuzzy sets. Utilizing this concept we present a Type-2 FCM (T2FCM) that is an extension of the conventional FCM. The experimental results show that the T2FCM is less susceptible to noise than the Type-1 FCM. The T2FCM can ignore the inlier and outlier interrupt. The clustering results show the robustness of the proposed T2FCM because a reasonable amount of noise data does not affect its clustering performance.

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16

Choi, Kil-Soo, Byung-In Choi, and Chung-Hoon Rhee. "A Kernel based Possibilistic Approach for Clustering and Image Segmentation." Journal of Korean Institute of Intelligent Systems 14, no. 7 (December 1, 2004): 889–94. http://dx.doi.org/10.5391/jkiis.2004.14.7.889.

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17

Zhou, Jian, Qina Wang, C. C. Hung, and Xiajie Yi. "Credibilistic Clustering: The Model and Algorithms." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no. 04 (August 2015): 545–64. http://dx.doi.org/10.1142/s0218488515500245.

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Анотація:

Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy [Formula: see text]-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concept of degree of belongingness, and PCAs suffer from the “coincident problem” and cannot provide very stable results for some data sets. In this study, we propose a new clustering algorithm, called the credibilistic clustering algorithm (CCA), based on the credibility measure. The credibility measure provides some unique properties which can solve the “coincident problem” and noise issue compared with the probability measure and possibility measure. Based on some randomly generated data sets, experimental results compared with FCM and PCA show that CCA can deal with the “coincident problem” with good clustering results, and it is more robust to noise than PCA.

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18

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 (September 20, 2018): 747–58. http://dx.doi.org/10.20965/jaciii.2018.p0747.

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Анотація:

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-clustering induced by multinomial mixture models (FCCMM) was proposed and extended to noise FCCMM (NFCCMM) in an analogous fashion to the NFCM. Ubukata et al. have proposed noise clustering-based possibilistic co-clustering induced by multinomial mixture models (NPCCMM) in an analogous fashion to the PCM. In this study, we develop an NPCCMM scheme considering variable cluster volumes and the fuzziness degree of item memberships to investigate the specific aspects of fuzzy nature rather than probabilistic nature in co-clustering tasks. We investigated the characteristics of the proposed NPCCMM by applying it to an artificial data set and conducted document clustering experiments using real-life data sets. As a result, we found that the proposed method can derive more flexible possibilistic partitions than the probabilistic model by adjusting the fuzziness degrees of object and item memberships. The document clustering experiments also indicated the effectiveness of tuning the fuzziness degree of object and item memberships, and the optimization of cluster volumes to improve classification performance.

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19

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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Анотація:

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 PCM algorithm based on machine learning to extract valuable features from thousands of dimensions, which increases the computational complexity and useless time consumption and makes it difficult to avoid the quality problem of clustering. To this end, this paper proposes a deep possibilistic C -mean clustering algorithm (DPCM) that combines the traditional PCM algorithm with a special deep network called autoencoder. Taking advantage of the fact that the autoencoder can minimize the reconstruction loss and the PCM uses soft affiliation to facilitate gradient descent, DPCM allows deep neural networks and PCM’s clustering centers to be optimized at the same time, so that it effectively improves the clustering efficiency and accuracy. Experiments on medical datasets with various dimensions demonstrate that this method has a better effect than traditional clustering methods, besides being able to overcome the interference of noise better.

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20

Yu, Hong, and Hu Luo. "A novel possibilistic fuzzy leader clustering algorithm." International Journal of Hybrid Intelligent Systems 8, no. 1 (March 18, 2011): 31–40. http://dx.doi.org/10.3233/his-2011-0129.

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21

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 (December 31, 2019): 250–62. http://dx.doi.org/10.5391/ijfis.2019.19.4.250.

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22

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 (April 2014): 263–91. http://dx.doi.org/10.1142/s0218488514500135.

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Анотація:

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, is proposed. Experiments with artificial and real symbolic interval-type data sets are used to evaluate the methods. The results of the proposed methods are better than the traditional soft clustering ones.

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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 (November 5, 2019): 143–59. http://dx.doi.org/10.1142/s0218488519400075.

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Анотація:

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 to the presence of noise, PCM has no chance to recover and produce a fine partition. This paper proposes a two-stage c-means clustering algorithm to tackle with most problems enumerated above. In the first stage called initialization, FCM with two modifications is performed: (1) extra cluster added for noisy data; (2) extra variable and constraint added to handle clusters of various diameters. In the second stage, a modified PCM algorithm is carried out, which also contains the cluster width tuning mechanism based on which it adaptively updates the possibilistic penalty terms. The proposed algorithm has less parameters than PCM when the number of clusters is [Formula: see text]. Numerical evaluation involving synthetic and standard test data sets proved the advantages of the proposed clustering model.

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24

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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25

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 fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM). The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.

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26

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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27

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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28

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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29

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

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30

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 (August 2005): 517–30. http://dx.doi.org/10.1109/tfuzz.2004.840099.

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31

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

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32

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

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33

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

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34

Yu, Misun, and Yu-Seung Ma. "Possibility of cost reduction by mutant clustering according to the clustering scope." Software Testing, Verification and Reliability 29, no. 1-2 (November 22, 2018): e1692. http://dx.doi.org/10.1002/stvr.1692.

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35

Ren, Jiaqi, and Youlong Yang. "Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource features." Neural Computing and Applications 32, no. 9 (November 8, 2018): 4785–804. http://dx.doi.org/10.1007/s00521-018-3851-0.

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36

Thiyagarajan, V. S., and Venkatachalapathy Venkatachalapathy. "Privacy Preserving Probabilistic Possibilistic Fuzzy C Means Clustering." Research Journal of Applied Sciences, Engineering and Technology 11, no. 1 (September 5, 2015): 27–39. http://dx.doi.org/10.19026/rjaset.11.1672.

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37

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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Анотація:

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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38

A.Viattchenin, Dmitri, and Stanislau Shyrai. "Intuitionistic Heuristic Prototype-based Algorithm of Possibilistic Clustering." Communications on Applied Electronics 1, no. 8 (May 26, 2015): 30–40. http://dx.doi.org/10.5120/cae-1629.

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39

Fadhel, Mohamed, and Adel M. "Selecting Parameters of the Fuzzy Possibilistic Clustering Algorithm." Communications on Applied Electronics 5, no. 10 (September 26, 2016): 42–52. http://dx.doi.org/10.5120/cae2016652389.

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40

Yang, Miin-Shen, Shou-Jen Chang-Chien, and Yessica Nataliani. "A Fully-Unsupervised Possibilistic C-Means Clustering Algorithm." IEEE Access 6 (2018): 78308–20. http://dx.doi.org/10.1109/access.2018.2884956.

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41

Chatzis, Sotirios P., and Gavriil Tsechpenakis. "A possibilistic clustering approach toward generative mixture models." Pattern Recognition 45, no. 5 (May 2012): 1819–25. http://dx.doi.org/10.1016/j.patcog.2011.10.010.

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42

Tjhi, William-Chandra, and Lihui Chen. "Possibilistic fuzzy co-clustering of large document collections." Pattern Recognition 40, no. 12 (December 2007): 3452–66. http://dx.doi.org/10.1016/j.patcog.2007.04.017.

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43

Xie, Zhenping, Shitong Wang, and F. L. Chung. "An enhanced possibilistic C-Means clustering algorithm EPCM." Soft Computing 12, no. 6 (August 14, 2007): 593–611. http://dx.doi.org/10.1007/s00500-007-0231-6.

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Wu, Xiao-hong, and Jian-jiang Zhou. "Modified possibilistic clustering model based on kernel methods." Journal of Shanghai University (English Edition) 12, no. 2 (April 2008): 136–40. http://dx.doi.org/10.1007/s11741-008-0210-2.

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Ji, Zexuan, Yong Xia, Quansen Sun, and Guo Cao. "Interval-valued possibilistic fuzzy C-means clustering algorithm." Fuzzy Sets and Systems 253 (October 2014): 138–56. http://dx.doi.org/10.1016/j.fss.2013.12.011.

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Xie, Zhenping, Shitong Wang, Dian You Zhang, F. L. Chung, and Hanbin . "Image Segmentation Using The Enhanced Possibilistic Clustering Method." Information Technology Journal 6, no. 4 (May 1, 2007): 541–46. http://dx.doi.org/10.3923/itj.2007.541.546.

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Masulli, F., and S. Rovetta. "Soft transition from probabilistic to possibilistic fuzzy clustering." IEEE Transactions on Fuzzy Systems 14, no. 4 (August 2006): 516–27. http://dx.doi.org/10.1109/tfuzz.2006.876740.

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Zhou, Jian, Longbing Cao, and Nan Yang. "On the convergence of some possibilistic clustering algorithms." Fuzzy Optimization and Decision Making 12, no. 4 (May 16, 2013): 415–32. http://dx.doi.org/10.1007/s10700-013-9159-8.

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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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Анотація:

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 more robust to initializations and get more reasonable partitions. As a by-product, ERKPCM overcomes the problem of generating coincident clusters of KPCM. The contrast experimental results with conventional algorithms show that ERKPCM is more robust to initializations, and has a relatively high precision and efficiency.

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Truong, Hung Quoc, Long Thanh Ngo, and Long The Pham. "Interval Type-2 Fuzzy Possibilistic C-Means Clustering Based on Granular Gravitational Forces and Particle Swarm Optimization." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 3 (May 20, 2019): 592–601. http://dx.doi.org/10.20965/jaciii.2019.p0592.

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Анотація:

The interval type-2 fuzzy possibilistic C-means clustering (IT2FPCM) algorithm improves the performance of the fuzzy possibilistic C-means clustering (FPCM) algorithm by addressing high degrees of noise and uncertainty. However, the IT2FPCM algorithm continues to face drawbacks including sensitivity to cluster centroid initialization, slow processing speed, and the possibility of being easily trapped in local optima. To overcome these drawbacks and better address noise and uncertainty, we propose an IT2FPCM method based on granular gravitational forces and particle swarm optimization (PSO). This method is based on the idea of gravitational forces grouping the data points into granules and then processing clusters on a granular space using a hybrid algorithm of the IT2FPCM and PSO algorithms. The proposed method also determines the initial centroids by merging granules until the number of granules is equal to the number of clusters. By reducing the elements in the granular space, the proposed algorithms also significantly improve performance when clustering large datasets. Experimental results are reported on different datasets compared with other approaches to demonstrate the advantages of the proposed method.

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