Retracted Article: A New Cluster Validity Index for Fuzzy Clustering Based on Similarity Measure

Authors:
Mohammad Hossein Zarandi;Elahe Neshat;I. Burhan Türkşen
Affiliations:
Department of Industrial Engineering, Amirkabir University of Technology, (Polytechnic of Tehran) P.O. Box 15875-4413, Tehran, Iran;Department of Industrial Engineering, Amirkabir University of Technology, (Polytechnic of Tehran) P.O. Box 15875-4413, Tehran, Iran;Department of Mechanical and Industrial Engineering, University of Toronto, 5 King College Road, Toronto, M5S2H8, Canada
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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Abstract

In this paper, first, the main problems of some cluster validity indices when they have been applied to Gustafson and Kessel (GK) clustering approach are review. It is shown that most of these cluster validity indices have serious shortcomings to validate Gustafson Kessel algorithm. Then, a new cluster validity index based on a similarity measure of fuzzy clusters for validation of GK algorithm is presented. This new index is not based on a geometric distance and can determine the degree of correlation of the clusters. Finally, the proposed cluster validity index is tested and validated by using five sets of artificially generated data. The results show that the proposed cluster validity index is more efficient and realistic than the former traditional indices.