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

18 Citations (Scopus)

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 publicationIEEE International Conference on Neural Networks - Conference Proceedings
Pages3609-3616
Publication statusPublished - 2006
Externally publishedYes

Fingerprint

Dive into the research topics of 'Multi-objective data clustering using variable-length real jumping genes genetic algorithm and local search method'. Together they form a unique fingerprint.

Cite this