Genetic-guided semi-supervised clustering algorithm with instance-level constraints

Yi Hong, Hui Xiong, Sam Kwong, Qingsheng Ren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Semi-supervised clustering with instance-level constraints is one of the most active research topics in the areas of pattern recognition, machine learning and data mining. Several recent studies have shown that instance-level constraints can significantly increase accuracies of a variety of clustering algorithms. However, instance-level constraints may split the search space of the optimal clustering solution into pieces, thus significantly compound the difficulty of the search task. This paper explores a genetic approach to solve the problem of semi-supervised clustering with instance-level constraints. In particular, a novel semi-supervised clustering algorithm with instance-level constraints, termed as the hybrid geneticguided semi-supervised clustering algorithm with instancelevel constraints (Cop-HGA), is proposed. Cop-HGA uses a hybrid genetic algorithm to perform the search task of a high quality clustering solution that is able to draw a good balance between predefined clustering criterion and available instance-level background knowledge. The effectiveness of Cop-HGA is confirmed by experimental results on several real data sets with artificial instance-level constraints.

Original languageEnglish (US)
Title of host publicationGECCO'08
Subtitle of host publicationProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
PublisherAssociation for Computing Machinery
Pages1381-1388
Number of pages8
ISBN (Print)9781605581309
DOIs
StatePublished - 2008
Externally publishedYes
Event10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 - Atlanta, GA, United States
Duration: Jul 12 2008Jul 16 2008

Publication series

NameGECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Conference

Conference10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008
CountryUnited States
CityAtlanta, GA
Period7/12/087/16/08

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

Keywords

  • Genetic algorithms
  • Semi-supervised clustering

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