A competitive and cooperative learning approach to robust data clustering

Yiu Ming CHEUNG*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

14 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 languageEnglish
Pages131-136
Number of pages6
Publication statusPublished - 2004
EventProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Grindelwald, Switzerland
Duration: 23 Feb 200425 Feb 2004

Conference

ConferenceProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
Country/TerritorySwitzerland
CityGrindelwald
Period23/02/0425/02/04

Scopus Subject Areas

  • Engineering(all)

User-Defined Keywords

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

Fingerprint

Dive into the research topics of 'A competitive and cooperative learning approach to robust data clustering'. Together they form a unique fingerprint.

Cite this