MiniMax ε-stable cluster validity index for Type-2 fuzziness

In this paper, we concentrate on the usage of uncertainty associated with the level of fuzziness in determination of the number of clusters in FCM for any data set. We propose a MiniMax ε-stable cluster validity index based on the uncertainty associated with the level of fuzziness within the framewo...

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Bibliographic Details
Published inInformation sciences Vol. 184; no. 1; pp. 64 - 74
Main Authors Ozkan, Ibrahim, Türkşen, I. Burhan
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.02.2012
Subjects
Clustering
Clusters
Entropy
Fuzzy
Fuzzy logic
Fuzzy set theory
Minimax technique
Optimization
Optimum number of clusters
Type-2 fuzziness
Uncertainty
Uncertainty
Entropy
Optimum number of clusters
Type-2 fuzziness
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Summary:In this paper, we concentrate on the usage of uncertainty associated with the level of fuzziness in determination of the number of clusters in FCM for any data set. We propose a MiniMax ε-stable cluster validity index based on the uncertainty associated with the level of fuzziness within the framework of interval valued Type 2 fuzziness. If the data have a clustered structure, the optimum number of clusters may be assumed to have minimum uncertainty under upper and lower levels of fuzziness. Upper and lower values of the level of fuzziness for Fuzzy C-Mean (FCM) clustering methodology have been found as m = 2.6 and 1.4, respectively, in our previous studies. Our investigation shows that the stability of cluster centers with respect to the level of fuzziness is sufficient for the determination of the number of clusters.
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ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2011.07.036