A new cluster validity index for type-2 fuzzy c-means algorithm

Author

Mema Devi, O. ; Begum, Shahin Ara

Author_Institution

Dept. of Comput. Sci., Assam Univ., Silchar, India

fYear

2013

Firstpage

2049

Lastpage

2056

Abstract

Considering the growing application areas of type-2 fuzzy logic, this paper investigates the existing cluster validity indices that are suitable for FCM clustering algorithm. Based on the cluster fuzzy degree of FCM fuzzy set and the existing cluster validity indices, a new cluster validity index for the type-2 FCM called SM-index is proposed. The optimal partition or an optimal number of cluster, is obtained by maximizing the value of SM-index. The experimental results on the UCI and microarray data sets are reported to demonstrate the effectiveness of the proposed cluster validity index in appropriately determining the number of clusters.

Keywords

fuzzy logic; fuzzy set theory; pattern clustering; FCM clustering algorithm; SM-index; UCI; cluster fuzzy degree; cluster validity index; microarray data set; optimal partition; type-2 fuzzy c-means algorithm; type-2 fuzzy logic; Clustering algorithms; Fuzzy logic; Indexes; Informatics; Linear programming; Partitioning algorithms; Uncertainty; Clustering; validity index and type-2 membership;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on

Conference_Location

Mysore

Print_ISBN

978-1-4799-2432-5

Type

conf

DOI

10.1109/ICACCI.2013.6637497

Filename

6637497