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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| Veröffentlicht in: | Pattern recognition Jg. 37; H. 10; S. 2027 - 2037 |
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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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| 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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