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

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

32 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	The 18th International Conference on Pattern Recognition
Editors	Y.Y. TANG, S.P. WANG, G. LORETTE, D.S. YEUNG, H. YAN
Publisher	IEEE
Pages	1200-1203
Number of pages	4
Volume	4
ISBN (Print)	0769525210
DOIs	https://doi.org/10.1109/ICPR.2006.827
Publication status	Published - 2006
Externally published	Yes
Event	18th International Conference on Pattern Recognition - , Hong Kong Duration: 20 Aug 2006 → 24 Aug 2006

Conference

Conference	18th International Conference on Pattern Recognition
Abbreviated title	ICPR 2006
Country/Territory	Hong Kong
Period	20/08/06 → 24/08/06

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10.1109/ICPR.2006.827

Cite this

@inproceedings{73a8e323e61c46a7be48c8ae629c2b15,

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 = "4",

pages = "1200--1203",

editor = "Y.Y. TANG and S.P. WANG and G. LORETTE and D.S. YEUNG and H. YAN",

booktitle = "The 18th International Conference on Pattern Recognition",

publisher = "IEEE",

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 YY TANG, SP WANG, G LORETTE, DS YEUNG & H YAN (eds), The 18th International Conference on Pattern Recognition. vol. 4, IEEE, pp. 1200-1203, 18th International Conference on Pattern Recognition, Hong Kong, 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.
The 18th International Conference on Pattern Recognition. ed. / Y.Y. TANG; S.P. WANG; G. LORETTE; D.S. YEUNG; H. YAN. Vol. 4 IEEE, 2006. p. 1200-1203.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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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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U2 - 10.1109/ICPR.2006.827

DO - 10.1109/ICPR.2006.827

M3 - Conference paper (refereed)

SN - 0769525210

VL - 4

SP - 1200

EP - 1203

BT - The 18th International Conference on Pattern Recognition

A2 - TANG, Y.Y.

A2 - WANG, S.P.

A2 - LORETTE, G.

A2 - YEUNG, D.S.

A2 - YAN, H.

PB - IEEE

T2 - 18th International Conference on Pattern Recognition

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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