A cooperative and penalized competitive learning approach to gaussian mixture clustering

Yiu Ming CHEUNG*, Hong Jia

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Competitive learning approaches with penalization or cooperation mechanism have been applied to unsupervised data clustering due to their attractive ability of automatic cluster number selection. In this paper, we further investigate the properties of different competitive strategies and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to have good convergence speed, precision and robustness. Experiments on Gaussian mixture clustering are performed to investigate the proposed algorithm. The promising results demonstrate its superiority.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
Pages435-440
Number of pages6
EditionPART 3
DOIs
Publication statusPublished - 2010
Event20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece
Duration: 15 Sep 201018 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6354 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Artificial Neural Networks, ICANN 2010
Country/TerritoryGreece
CityThessaloniki
Period15/09/1018/09/10

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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