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Multi-objective data clustering using variable-length real jumping genes genetic algorithm and local search method

  • Kazi Shah Nawaz Ripon
  • , Chi-Ho Tsang
  • , Sam Kwong

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

In this paper, we present a novel multi-objective evolutionary clustering approach using Variable-length Real Jumping Genes Genetic Algorithms (VRJGGA). The proposed algorithm that extends Jumping Genes Genetic Algorithm (JGGA) [1] evolves clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. Some local search methods such as probabilistic cluster merging and splitting are introduced in VRJGGA for the clustering improvement. Experimental results based on several artificial and real-world data show that VRJGGA can obtain non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance. © 2006 IEEE.
Original languageEnglish
Title of host publicationThe 2006 IEEE International Joint Conference on Neural Network Proceedings
PublisherIEEE
Pages3609-3616
ISBN (Print)0780394909, 9780780394902
DOIs
Publication statusPublished - 2006
Event2006 International Joint Conference on Neural Networks (IJCNN '06) - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

Name
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

Conference2006 International Joint Conference on Neural Networks (IJCNN '06)
PlaceCanada
CityVancouver, BC
Period16/07/0621/07/06

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