An improved competitive and cooperative learning approach for data clustering

Shao Ping Wang*, Wen Jiang Pei, Yiu Ming CHEUNG

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

The recently proposed Competitive and Cooperative Learning algorithm(CCL) (Cheung 2004) has provided a promising way to perform the data clustering without knowing the number of clusters. Nevertheless, its performance is somewhat sensitive to the initialization of seed points. Also, its cooperative mechanism is applicable to the homogenous clusters only. In this paper, we will therefore suggest using the FSCL algorithm to initialize the seed points such that each cluster of data will at least have a seed point. Furthermore, we update the cooperation radius of seed points in CCL, whereby the improved CCL (ICCL for short) can be applicable to the heterogeneous clusters as well. Experiments show the efficacy of the proposed algorithm.

Original languageEnglish
Title of host publicationProceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007
Pages320-324
Number of pages5
DOIs
Publication statusPublished - 2007
Event2007 International Conference on Computational Intelligence and Security, CIS'07 - Harbin, Heilongjiang, China
Duration: 15 Dec 200719 Dec 2007

Publication series

NameProceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007

Conference

Conference2007 International Conference on Computational Intelligence and Security, CIS'07
Country/TerritoryChina
CityHarbin, Heilongjiang
Period15/12/0719/12/07

Scopus Subject Areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

Fingerprint

Dive into the research topics of 'An improved competitive and cooperative learning approach for data clustering'. Together they form a unique fingerprint.

Cite this