Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm

Kazi Shah Nawaz Ripon; Chi-Ho Tsang; Sam Kwong; Man-Ki Ip

doi:10.1109/ICPR.2006.827

Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm

Kazi Shah Nawaz Ripon, Chi-Ho Tsang, Sam Kwong, Man-Ki Ip

Department of Computer Science

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

33 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 near-optimal clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. 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 language	English
Title of host publication	Proceedings - International Conference on Pattern Recognition
Pages	1200-1203
Volume	1
DOIs	https://doi.org/10.1109/ICPR.2006.827
Publication status	Published - 2006
Event	18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China Duration: 20 Aug 2006 → 24 Aug 2006

Publication series

Name
Volume	1
ISSN (Print)	1051-4651

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Conference	18th International Conference on Pattern Recognition, ICPR 2006
Place	China
City	Hong Kong
Period	20/08/06 → 24/08/06

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@inproceedings{2cccd848a62f448a837da53026b674af,

title = "Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm",

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 near-optimal clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. 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. {\textcopyright} 2006 IEEE.",

author = "Ripon, {Kazi Shah Nawaz} and Chi-Ho Tsang and Sam Kwong and Man-Ki Ip",

year = "2006",

doi = "10.1109/ICPR.2006.827",

language = "English",

isbn = "0769525210",

volume = "1",

pages = "1200--1203",

booktitle = "Proceedings - International Conference on Pattern Recognition",

note = "18th International Conference on Pattern Recognition, ICPR 2006 ; Conference date: 20-08-2006 Through 24-08-2006",

}

Ripon, KSN, Tsang, C-H, Kwong, S & Ip, M-K 2006, Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm. in Proceedings - International Conference on Pattern Recognition. vol. 1, 1699105, pp. 1200-1203, 18th International Conference on Pattern Recognition, ICPR 2006, Hong Kong, China, 20/08/06. https://doi.org/10.1109/ICPR.2006.827

Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm. / Ripon, Kazi Shah Nawaz; Tsang, Chi-Ho; Kwong, Sam et al.
Proceedings - International Conference on Pattern Recognition. Vol. 1 2006. p. 1200-1203 1699105.

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

TY - GEN

T1 - Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm

AU - Ripon, Kazi Shah Nawaz

AU - Tsang, Chi-Ho

AU - Kwong, Sam

AU - Ip, Man-Ki

PY - 2006

Y1 - 2006

N2 - 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 near-optimal clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. 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.

AB - 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 near-optimal clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. 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.

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M3 - RGC 32 - Refereed conference paper (with host publication)

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SN - 9780769525211

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BT - Proceedings - International Conference on Pattern Recognition

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Y2 - 20 August 2006 through 24 August 2006

ER -

Multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm

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