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

doi:10.1109/IJCNN.2006.247372

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 Proceedings › Conference paper (refereed) › Research › peer-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 language	English
Title of host publication	The 2006 IEEE International Joint Conference on Neural Network Proceedings
Publisher	IEEE
Pages	3609-3616
Number of pages	8
ISBN (Print)	0780394909
DOIs	https://doi.org/10.1109/IJCNN.2006.247372
Publication status	Published - 2006
Externally published	Yes
Event	International Joint Conference on Neural Networks 2006 - Vancouver, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Conference

Conference	International Joint Conference on Neural Networks 2006
Abbreviated title	IJCNN '06
Country/Territory	Canada
City	Vancouver
Period	16/07/06 → 21/07/06

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10.1109/IJCNN.2006.247372

Cite this

@inproceedings{3acf3522e2da4358b77aff2220b739ac,

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

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. {\textcopyright} 2006 IEEE.",

author = "RIPON, {Kazi Shah Nawaz} and Chi-Ho TSANG and Sam KWONG",

year = "2006",

doi = "10.1109/IJCNN.2006.247372",

language = "English",

isbn = "0780394909",

pages = "3609--3616",

booktitle = "The 2006 IEEE International Joint Conference on Neural Network Proceedings",

publisher = "IEEE",

note = "International Joint Conference on Neural Networks 2006, IJCNN '06 ; Conference date: 16-07-2006 Through 21-07-2006",

}

RIPON, KSN, TSANG, C-H & KWONG, S 2006, Multi-objective data clustering using variable-length real jumping genes genetic algorithm and local search method. in The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, pp. 3609-3616, International Joint Conference on Neural Networks 2006, Vancouver, British Columbia, Canada, 16/07/06. https://doi.org/10.1109/IJCNN.2006.247372

Multi-objective data clustering using variable-length real jumping genes genetic algorithm and local search method. / RIPON, Kazi Shah Nawaz; TSANG, Chi-Ho; KWONG, Sam.
The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. p. 3609-3616.

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

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AU - TSANG, Chi-Ho

AU - KWONG, Sam

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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 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.

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 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.

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DO - 10.1109/IJCNN.2006.247372

M3 - Conference paper (refereed)

SN - 0780394909

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EP - 3616

BT - The 2006 IEEE International Joint Conference on Neural Network Proceedings

PB - IEEE

T2 - International Joint Conference on Neural Networks 2006

Y2 - 16 July 2006 through 21 July 2006

ER -

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

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