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

Yi HONG, Hui XIONG, Sam KWONG, Qingsheng REN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationGECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
Pages471-472
Publication statusPublished - 2008
Externally publishedYes

Keywords

  • Data clustering
  • Similarity matrix encoding strategy
  • Virtual population based incremental learning algorithm

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