Data clustering using virtual population based incremental learning algorithm with similarity matrix encoding strategy

Authors:
Yi Hong;Sam Kwong;Hui Xiong;Qingsheng Ren
Affiliations:
City University of Hong Kong, Hong Kong, Hong Kong;City University of Hong Kong, Hong Kong, Hong Kong;Rutgers University, New Jersey, NJ, USA;Shanghai Jiao Tong University, Shanghai, China
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

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Abstract

Data clustering is a good benchmark problem for testing the performance of many combinatory optimization methods. However, very few works have been done on using the estimation of distribution algorithms for solving the problem of data clustering. The purpose of this paper is to demonstrate the effectiveness of the estimation of distribution algorithms for solving the problem of data clustering. In particular, a novel encoding strategy termed as the Similarity Matrix Encoding strategy (SME) and a Virtual Population Based Incremental Learning algorithm using SME encoding strategy (VPBIL-SME) are proposed for clustering a set of unlabeled instances into groups. Effectiveness of VPBIL-SME is confirmed by experimental results on several real data sets.