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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Bibliographic Details
Published in:Pattern recognition Vol. 37; no. 10; pp. 2027 - 2037
Main Authors: Sun, Haojun, Wang, Shengrui, Jiang, Qingshan
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2004
Subjects:
Clustering
Fuzzy C-means
Overlapping clusters
Validity index
Overlapping clusters
Fuzzy C-means
Clustering
Validity index
ISSN:0031-3203, 1873-5142
Online Access:Get full text
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Summary: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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2004.03.012