Conference Proceedings

Cluster Validity For Kernel Fuzzy Clustering

Timothy C Havens, James C Bezdek, Marimuthu Palaniswami

2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | IEEE | Published : 2012

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. © 2012 IEEE.

Grants

Awarded by National Science Foundation


Funding Acknowledgements

Havens is supported by the National Science Foundation under Grant #1019343 to the Computing Research Association for the CI Fellows Project. This material is based upon work supported by the Australian Research Council.