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: Contribution to conference › Conference paper › peer-review

15 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
Pages	131-136
Number of pages	6
Publication status	Published - 2004
Event	Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Grindelwald, Switzerland Duration: 23 Feb 2004 → 25 Feb 2004

Conference

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

Scopus Subject Areas

Engineering(all)

User-Defined Keywords

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

Cite this

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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.; Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence ; Conference date: 23-02-2004 Through 25-02-2004",

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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

UR - http://www.scopus.com/inward/record.url?scp=10944248619&partnerID=8YFLogxK

M3 - Conference paper

AN - SCOPUS:10944248619

SP - 131

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T2 - Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence

Y2 - 23 February 2004 through 25 February 2004

ER -

A competitive and cooperative learning approach to robust data clustering

Abstract

Conference

Scopus Subject Areas

User-Defined Keywords

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