FCM-Based Model Selection Algorithms for Determining the Number of Clusters
Clustering is an important research topic that has practical applications in many fields. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however,...
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| Published in: | Pattern recognition Vol. 37; no. 10; pp. 2027 - 2037 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.10.2004
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | Clustering is an important research topic that has practical applications in many fields. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initializing parameters. The main goal of this paper is to develop an effective fuzzy algorithm for automatically determining the number of clusters. After a brief review of the relevant literature, we present a new algorithm for determining the number of clusters in a given data set and a new validity index for measuring the “goodness” of clustering. Experimental results and comparisons are given to illustrate the performance of the new algorithm. |
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| AbstractList | Clustering is an important research topic that has practical applications in many fields. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initializing parameters. The main goal of this paper is to develop an effective fuzzy algorithm for automatically determining the number of clusters. After a brief review of the relevant literature, we present a new algorithm for determining the number of clusters in a given data set and a new validity index for measuring the “goodness” of clustering. Experimental results and comparisons are given to illustrate the performance of the new algorithm. |
| Author | Sun, Haojun Wang, Shengrui Jiang, Qingshan |
| Author_xml | – sequence: 1 givenname: Haojun surname: Sun fullname: Sun, Haojun email: sun@dmi.usherb.ca organization: Department of Computer Science, Faculty of Sciences, University of Sherbrooke, Sherbrooke, QC, Canada, J1K 2R1 – sequence: 2 givenname: Shengrui surname: Wang fullname: Wang, Shengrui email: wang@dmi.usherb.ca organization: Department of Computer Science, Faculty of Sciences, University of Sherbrooke, Sherbrooke, QC, Canada, J1K 2R1 – sequence: 3 givenname: Qingshan surname: Jiang fullname: Jiang, Qingshan email: qjiang@xmu.edu.cn organization: Department of Computer Science, Xiamen University, Fujian 361005, China |
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| Cites_doi | 10.1142/9789814343138_0001 10.1016/0304-3975(85)90224-5 10.1016/S0167-8655(98)00142-1 10.1016/S0167-8655(99)00069-0 10.1109/72.159056 10.1016/S0167-8655(97)00168-2 10.1016/0165-0114(94)90242-9 10.1109/3477.809032 10.1109/91.413225 10.1109/34.85677 10.1109/83.541421 |
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| Keywords | Overlapping clusters Fuzzy C-means Clustering Validity index |
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| References | Aitnouri, Dubeau, Wang, Ziou (BIB20) 2002; 12 Han, Kamber (BIB1) 2001 Everitt (BIB4) 1993 Y. Fukuyama, M. Sugeno, A new method of choosing the number of clusters for the fuzzy C-means method, in: Proceedings of Fifth Fuzzy Systems Symposium, 1989, pp. 247–250. Pena, Lozano, Larranaga (BIB13) 1999; 20 Gonzalez (BIB14) 1985; 38 Rezae, Letlieveldt, Reiber (BIB10) 1998; 19 Halgamuge, Glesner (BIB18) 1994; 65 F. Hoppner et al., Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, Wiley, New York, 1999. Aitnouri, Wang, Ziou (BIB19) 2000; 10 Jain, Dubes (BIB5) 1988 Abeantes, Marques (BIB6) 1996; 5 Krishnapuram, Nasraouui, Keller (BIB7) 1992; 3 H. Rhee, K. Oh, A validity measure for fuzzy clustering and its use in selecting optimal number of clusters, in: Proc. IEEE, 1996, pp. 1020–1025. J.C. Bezdek, Pattern Recognition in Handbook of Fuzzy Computation. IOP Publishing Ltd., Boston, Ny, 1998 (Chapter F6). Xie, Beni (BIB8) 1991; 13 R. Dubes, Cluster analysis and related issues, Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co., Inc., River Edger, NJ, USA, 1993, pp. 3–32. Pal, Bezdek (BIB17) 1995; 3 Baraldi, Blonda (BIB12) 1999; SMC-29 Zahid, Abouelala (BIB16) 1999; 20 Aitnouri (10.1016/j.patcog.2004.03.012_BIB19) 2000; 10 Gonzalez (10.1016/j.patcog.2004.03.012_BIB14) 1985; 38 Halgamuge (10.1016/j.patcog.2004.03.012_BIB18) 1994; 65 10.1016/j.patcog.2004.03.012_BIB3 10.1016/j.patcog.2004.03.012_BIB2 Zahid (10.1016/j.patcog.2004.03.012_BIB16) 1999; 20 Han (10.1016/j.patcog.2004.03.012_BIB1) 2001 10.1016/j.patcog.2004.03.012_BIB11 Everitt (10.1016/j.patcog.2004.03.012_BIB4) 1993 10.1016/j.patcog.2004.03.012_BIB15 Jain (10.1016/j.patcog.2004.03.012_BIB5) 1988 Rezae (10.1016/j.patcog.2004.03.012_BIB10) 1998; 19 Krishnapuram (10.1016/j.patcog.2004.03.012_BIB7) 1992; 3 Pena (10.1016/j.patcog.2004.03.012_BIB13) 1999; 20 Baraldi (10.1016/j.patcog.2004.03.012_BIB12) 1999; SMC-29 10.1016/j.patcog.2004.03.012_BIB9 Abeantes (10.1016/j.patcog.2004.03.012_BIB6) 1996; 5 Pal (10.1016/j.patcog.2004.03.012_BIB17) 1995; 3 Aitnouri (10.1016/j.patcog.2004.03.012_BIB20) 2002; 12 Xie (10.1016/j.patcog.2004.03.012_BIB8) 1991; 13 |
| References_xml | – volume: 5 start-page: 1507 year: 1996 end-page: 1521 ident: BIB6 article-title: A class of constrained clustering for object boundary extraction publication-title: IEEE Trans. Image Process. – volume: 19 start-page: 237 year: 1998 end-page: 246 ident: BIB10 article-title: A new cluster validity index for the fuzzy c-means publication-title: Pattern Recogn. Lett. – volume: 20 start-page: 1027 year: 1999 end-page: 1040 ident: BIB13 article-title: An empirical comparison of four initialization methods for the publication-title: Pattern Recogn. Lett. – reference: J.C. Bezdek, Pattern Recognition in Handbook of Fuzzy Computation. IOP Publishing Ltd., Boston, Ny, 1998 (Chapter F6). – volume: 20 start-page: 123 year: 1999 end-page: 129 ident: BIB16 article-title: Unsupervised fuzzy clustering publication-title: Pattern Recognition Lett. – reference: Y. Fukuyama, M. Sugeno, A new method of choosing the number of clusters for the fuzzy C-means method, in: Proceedings of Fifth Fuzzy Systems Symposium, 1989, pp. 247–250. – reference: H. Rhee, K. Oh, A validity measure for fuzzy clustering and its use in selecting optimal number of clusters, in: Proc. IEEE, 1996, pp. 1020–1025. – volume: 13 start-page: 841 year: 1991 end-page: 847 ident: BIB8 article-title: A validity measure for fuzzy clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) – volume: SMC-29 start-page: 778 year: 1999 end-page: 785 ident: BIB12 article-title: A survey of fuzzy clustering algorithms for pattern recognition—part I publication-title: IEEE Trans. Syst. Man, Cybern. – volume: 3 start-page: 370 year: 1995 end-page: 390 ident: BIB17 article-title: On cluster validity for the fuzzy C-means model publication-title: IEEE Trans. Fuzzy Systems – year: 2001 ident: BIB1 publication-title: Datamining: Concepts and Techniques – volume: 12 start-page: 331 year: 2002 end-page: 346 ident: BIB20 article-title: Controlling mixture component overlap for clustering algorithms evaluation publication-title: Pattern Recognition and Image Analysis – year: 1993 ident: BIB4 publication-title: Cluster Analysis – volume: 3 start-page: 663 year: 1992 end-page: 671 ident: BIB7 article-title: The fuzzy C-spherical shells algorithm: a new approach publication-title: IEEE Trans. Neural Networks – volume: 38 start-page: 293 year: 1985 end-page: 306 ident: BIB14 article-title: Clustering to minimize and maximum intercluster distance publication-title: Theor. Comput. Sci. – year: 1988 ident: BIB5 publication-title: Algorithms for Clustering Data – volume: 65 start-page: 1 year: 1994 end-page: 12 ident: BIB18 article-title: Neural networks in designing fuzzy systems for real world applications publication-title: Fuzzy Sets Systems – reference: R. Dubes, Cluster analysis and related issues, Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co., Inc., River Edger, NJ, USA, 1993, pp. 3–32. – reference: F. Hoppner et al., Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, Wiley, New York, 1999. – volume: 10 start-page: 206 year: 2000 end-page: 217 ident: BIB19 article-title: On clustering techniques comparison for histogram pdf estimation publication-title: J. Pattern Recognition Image Anal. – ident: 10.1016/j.patcog.2004.03.012_BIB3 doi: 10.1142/9789814343138_0001 – volume: 38 start-page: 293 year: 1985 ident: 10.1016/j.patcog.2004.03.012_BIB14 article-title: Clustering to minimize and maximum intercluster distance publication-title: Theor. Comput. Sci. doi: 10.1016/0304-3975(85)90224-5 – volume: 20 start-page: 123 year: 1999 ident: 10.1016/j.patcog.2004.03.012_BIB16 article-title: Unsupervised fuzzy clustering publication-title: Pattern Recognition Lett. doi: 10.1016/S0167-8655(98)00142-1 – ident: 10.1016/j.patcog.2004.03.012_BIB11 – ident: 10.1016/j.patcog.2004.03.012_BIB2 – year: 2001 ident: 10.1016/j.patcog.2004.03.012_BIB1 – volume: 20 start-page: 1027 year: 1999 ident: 10.1016/j.patcog.2004.03.012_BIB13 article-title: An empirical comparison of four initialization methods for the k-means algorithm publication-title: Pattern Recogn. Lett. doi: 10.1016/S0167-8655(99)00069-0 – ident: 10.1016/j.patcog.2004.03.012_BIB15 – volume: 12 start-page: 331 issue: 4 year: 2002 ident: 10.1016/j.patcog.2004.03.012_BIB20 article-title: Controlling mixture component overlap for clustering algorithms evaluation publication-title: Pattern Recognition and Image Analysis – volume: 3 start-page: 663 issue: 5 year: 1992 ident: 10.1016/j.patcog.2004.03.012_BIB7 article-title: The fuzzy C-spherical shells algorithm: a new approach publication-title: IEEE Trans. Neural Networks doi: 10.1109/72.159056 – volume: 19 start-page: 237 year: 1998 ident: 10.1016/j.patcog.2004.03.012_BIB10 article-title: A new cluster validity index for the fuzzy c-means publication-title: Pattern Recogn. Lett. doi: 10.1016/S0167-8655(97)00168-2 – volume: 65 start-page: 1 issue: 1 year: 1994 ident: 10.1016/j.patcog.2004.03.012_BIB18 article-title: Neural networks in designing fuzzy systems for real world applications publication-title: Fuzzy Sets Systems doi: 10.1016/0165-0114(94)90242-9 – volume: SMC-29 start-page: 778 year: 1999 ident: 10.1016/j.patcog.2004.03.012_BIB12 article-title: A survey of fuzzy clustering algorithms for pattern recognition—part I publication-title: IEEE Trans. Syst. Man, Cybern. doi: 10.1109/3477.809032 – year: 1993 ident: 10.1016/j.patcog.2004.03.012_BIB4 – year: 1988 ident: 10.1016/j.patcog.2004.03.012_BIB5 – ident: 10.1016/j.patcog.2004.03.012_BIB9 – volume: 3 start-page: 370 issue: 3 year: 1995 ident: 10.1016/j.patcog.2004.03.012_BIB17 article-title: On cluster validity for the fuzzy C-means model publication-title: IEEE Trans. Fuzzy Systems doi: 10.1109/91.413225 – volume: 13 start-page: 841 issue: 8 year: 1991 ident: 10.1016/j.patcog.2004.03.012_BIB8 article-title: A validity measure for fuzzy clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) doi: 10.1109/34.85677 – volume: 5 start-page: 1507 year: 1996 ident: 10.1016/j.patcog.2004.03.012_BIB6 article-title: A class of constrained clustering for object boundary extraction publication-title: IEEE Trans. Image Process. doi: 10.1109/83.541421 – volume: 10 start-page: 206 issue: 2 year: 2000 ident: 10.1016/j.patcog.2004.03.012_BIB19 article-title: On clustering techniques comparison for histogram pdf estimation publication-title: J. Pattern Recognition Image Anal. |
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| SubjectTerms | Clustering Fuzzy C-means Overlapping clusters Validity index |
| Title | FCM-Based Model Selection Algorithms for Determining the Number of Clusters |
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