Cluster validity for kernel fuzzy clustering

Author

Havens, Timothy C. ; Bezdek, James C. ; Palaniswami, Marimuthu

Author_Institution

Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

8

Abstract

This paper presents cluster validity for kernel fuzzy clustering. First, we describe existing cluster validity indices that can be directly applied to partitions obtained by kernel fuzzy clustering algorithms. Second, we show how validity indices that take dissimilarity (or relational) data D as input can be applied to kernel fuzzy clustering. Third, we present four propositions that allow other existing cluster validity indices to be adapted to kernel fuzzy partitions. As an example of how these propositions are used, five well-known indices are formulated.We demonstrate several indices for kernel fuzzy c-means (kFCM) partitions of both synthetic and real data.

Keywords

fuzzy set theory; pattern clustering; cluster validity; dissimilarity data; kFCM partitions; kernel fuzzy c-means partitions; kernel fuzzy clustering; real data; synthetic data; Clustering algorithms; Equations; Indexes; Kernel; Partitioning algorithms; Probabilistic logic; Vectors; cluster validity; fuzzy clustering; kernel clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on

Conference_Location

Brisbane, QLD

ISSN

1098-7584

Print_ISBN

978-1-4673-1507-4

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZ-IEEE.2012.6250820

Filename

6250820