A competitive and cooperative learning approach to robust data clustering

Yiu Ming Cheung

A competitive and cooperative learning approach to robust data clustering

Yiu Ming Cheung^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

16 Citations (Scopus)

Abstract

This paper presents a new semi-competitive learning paradigm named Competitive and Cooperative Learning (CCL), in which seed points not only compete each other for updating to adapt to an input each time, but also dynamically cooperate to achieve the learning task. This competitive and cooperative mechanism can automatically merge those extra seed points, meanwhile making the seed points gradually converge to the corresponding cluster centers. Consequently, CCL can perform a robust clustering analysis without prior knowing the exact cluster number so long as the number of seed points is not less than the true one. The experiments have successfully shown its outstanding performance on data clustering.

Original language	English
Title of host publication	Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence 2004
Publisher	International Conference on Neural Networks and Computational Intelligence
Pages	131-136
Number of pages	6
Publication status	Published - Feb 2004
Event	IASTED International Conference on Neural Networks and Computational Intelligence 2004 - Grindelwald, Switzerland Duration: 23 Feb 2004 → 25 Feb 2004

Publication series

Name	Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence

Conference

Conference	IASTED International Conference on Neural Networks and Computational Intelligence 2004
Country/Territory	Switzerland
City	Grindelwald
Period	23/02/04 → 25/02/04

Scopus Subject Areas

General Engineering

User-Defined Keywords

Cluster Number
Clustering Analysis
Cooperative and Competitive Learning
Rival Penalization Controlled Competitive Learning
Semi-Competitive Learning

Cite this

Cheung, Y. M. (2004). A competitive and cooperative learning approach to robust data clustering. In Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence 2004 (pp. 131-136). (Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence). International Conference on Neural Networks and Computational Intelligence.

Cheung, Yiu Ming. / A competitive and cooperative learning approach to robust data clustering. Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence 2004. International Conference on Neural Networks and Computational Intelligence, 2004. pp. 131-136 (Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence).

@inproceedings{ba97e49179074b3e84d7e3362219975f,

title = "A competitive and cooperative learning approach to robust data clustering",

abstract = "This paper presents a new semi-competitive learning paradigm named Competitive and Cooperative Learning (CCL), in which seed points not only compete each other for updating to adapt to an input each time, but also dynamically cooperate to achieve the learning task. This competitive and cooperative mechanism can automatically merge those extra seed points, meanwhile making the seed points gradually converge to the corresponding cluster centers. Consequently, CCL can perform a robust clustering analysis without prior knowing the exact cluster number so long as the number of seed points is not less than the true one. The experiments have successfully shown its outstanding performance on data clustering.",

keywords = "Cluster Number, Clustering Analysis, Cooperative and Competitive Learning, Rival Penalization Controlled Competitive Learning, Semi-Competitive Learning",

author = "Cheung, {Yiu Ming}",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; IASTED International Conference on Neural Networks and Computational Intelligence 2004 ; Conference date: 23-02-2004 Through 25-02-2004",

year = "2004",

month = feb,

language = "English",

series = "Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence",

publisher = "International Conference on Neural Networks and Computational Intelligence",

pages = "131--136",

booktitle = "Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence 2004",

}

Cheung, YM 2004, A competitive and cooperative learning approach to robust data clustering. in Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence 2004. Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence, International Conference on Neural Networks and Computational Intelligence, pp. 131-136, IASTED International Conference on Neural Networks and Computational Intelligence 2004, Grindelwald, Switzerland, 23/02/04.

A competitive and cooperative learning approach to robust data clustering. / Cheung, Yiu Ming.
Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence 2004. International Conference on Neural Networks and Computational Intelligence, 2004. p. 131-136 (Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Cheung, Yiu Ming

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N2 - This paper presents a new semi-competitive learning paradigm named Competitive and Cooperative Learning (CCL), in which seed points not only compete each other for updating to adapt to an input each time, but also dynamically cooperate to achieve the learning task. This competitive and cooperative mechanism can automatically merge those extra seed points, meanwhile making the seed points gradually converge to the corresponding cluster centers. Consequently, CCL can perform a robust clustering analysis without prior knowing the exact cluster number so long as the number of seed points is not less than the true one. The experiments have successfully shown its outstanding performance on data clustering.

AB - This paper presents a new semi-competitive learning paradigm named Competitive and Cooperative Learning (CCL), in which seed points not only compete each other for updating to adapt to an input each time, but also dynamically cooperate to achieve the learning task. This competitive and cooperative mechanism can automatically merge those extra seed points, meanwhile making the seed points gradually converge to the corresponding cluster centers. Consequently, CCL can perform a robust clustering analysis without prior knowing the exact cluster number so long as the number of seed points is not less than the true one. The experiments have successfully shown its outstanding performance on data clustering.

KW - Cluster Number

KW - Clustering Analysis

KW - Cooperative and Competitive Learning

KW - Rival Penalization Controlled Competitive Learning

KW - Semi-Competitive Learning

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ER -

Cheung YM. A competitive and cooperative learning approach to robust data clustering. In Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence 2004. International Conference on Neural Networks and Computational Intelligence. 2004. p. 131-136. (Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence).

A competitive and cooperative learning approach to robust data clustering

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

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Cite this