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

Yi HONG, Hui XIONG, Sam KWONG, Qingsheng REN

Research output: Other Conference ContributionsPosterpeer-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
Pages471-472
Number of pages2
DOIs
Publication statusPublished - Jul 2008
Externally publishedYes
Event10th Annual Genetic and Evolutionary Computation Conference - Atlanta, United States
Duration: 12 Jul 200816 Jul 2008

Conference

Conference10th Annual Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO 2008
Country/TerritoryUnited States
CityAtlanta
Period12/07/0816/07/08

Keywords

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

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