Title

A Fuzzy Gap Statistic For Fuzzy C-Means

Keywords

Cluster validity; Fuzzy C-Means; Gap statistic

Abstract

The gap statistic is a statistical method for determining the number of optimal clusters for an unsupervised clustering algorithm and has been shown to outperform other cluster validity indices for the K-means clustering algorithm. In this paper, we assess the performance of the gap statistic when applied to the Fuzzy C-Means (FCM) algorithm and introduce a fuzzy gap statistic. We compare the gap statistic performance versus the partition coefficient and entropy indices introduced by Bezdek, the Xie-Beni and extended Xie-Beni indices, and the Fukuyama-Sugeno index. We show that the fuzzy gap statistic is more robust than the ordinary gap statistic for the IRIS data set, and we show promising results when comparing the gap statistic to the traditional fuzzy clustering indices.

Publication Date

12-1-2007

Publication Title

Proceedings of the 11th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2007

Number of Pages

68-73

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

54949142835 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/54949142835

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