A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution

Authors:
Wenyin Gong;Zhihua Cai;Charles X. Ling;Bo Huang
Affiliations:
School of Computer Science, China University of Geosciences, Wuhan, P.R. China 430074;School of Computer Science, China University of Geosciences, Wuhan, P.R. China 430074;Department of Computer Science, The University of Western Ontario, London, Canada;School of Computer Science, China University of Geosciences, Wuhan, P.R. China 430074
Venue:
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Year:
2009

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Abstract

Clustering is a core problem in data mining and machine learning though it is widely applied in many fields. Recently, it is very popular to use the evolutionary algorithm to solve the problem. This paper proposes an automatic clustering differential evolution (DE) technique for the problem. This approach can be characterized by (i) proposing a modified point symmetry-based cluster validity index (CVI) as a measure of the validity of the corresponding partitioning, (ii) using the Kd-tree based nearest neighbor search to reduce the complexity of finding the closest symmetric point, and (iii) employing a new representation to represent an individual. Experiments conducted on 6 artificial data sets of diverse complexities indicate that this approach is suitable for both the symmetrical intra-clusters and the symmetrical inter-clusters. In addition, it is able to find the optimal number of clusters of the data. Furthermore, based on the comparison with the original point symmetry-based CVI, this proposed point symmetry-based CVI shows better performance in terms of the F-measure and the number of clusters found.