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Assessing the Quality of Fuzzy Partitions Using Relative Intersection
Dae-Won KIM Young-il KIM Doheon LEE Kwang Hyung LEE
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2005/03/01
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Computation and Computational Models
cluster validity, fuzzy clustering, fuzzy c-means,
Full Text: PDF(433.1KB)
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In this paper, conventional validity indexes are reviewed and the shortcomings of the fuzzy cluster validation index based on inter-cluster proximity are examined. Based on these considerations, a new cluster validity index is proposed for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index is defined as the average value of the relative intersections of all possible pairs of fuzzy clusters in the system. It computes the overlap between two fuzzy clusters by considering the intersection of each data point in the overlap. The optimal number of clusters is obtained by minimizing the validity index with respect to c. Experiments in which the proposed validity index and several conventional validity indexes were applied to well known data sets highlight the superior qualities of the proposed index.