Automatically determining the number of clusters using decision-theoretic rough set

Authors:
Hong Yu;Zhanguo Liu;Guoyin Wang
Affiliations:
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

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Abstract

Clustering provides a common means of identifying structure in complex data, and there is renewed interest in clustering as a tool for the analysis of large data sets in many fields. A fundamental and difficult problem in cluster analysis is how many clusters are appropriate for the description of a given system. The objective of this paper is to develop a method for automatically determining the number of clusters. The method firstly proposes a new clustering validity evaluation function based on the extended decision-theoretic rough set model. Then a hierarchical clustering algorithm is proposed and some conclusions are obtained in the validation of the algorithm. Experimental results show that the new clustering method can stop at the perfect number of clusters automatically and validate the change laws of the clustering validity evaluation function.