On rival penalization controlled competitive learning for clustering with automatic cluster number selection

Yiu Ming CHEUNG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

62 Citations (Scopus)

Abstract

The existing Rival Penalized Competitive Learning (RPCL) algorithm and its variants have provided an attractive way to perform data clustering without knowing the exact number of clusters. However, their performance is sensitive to the preselection of the rival delearning rate. In this paper, we further investigate the RPCL and present a mechanism to control the strength of rival penalization dynamically. Consequently, we propose the Rival Penalization Controlled Competitive Learning (RPCCL) algorithm and its stochastic version. In each of these algorithms, the selection of the delearning rate is circumvented using a novel technique. We compare the performance of RPCCL to RPCL in Gaussian mixture clustering and color image segmentation, respectively. The experiments have produced the promising results.

Original languageEnglish
Pages (from-to)1583-1588
Number of pages6
JournalIEEE Transactions on Knowledge and Data Engineering
Volume17
Issue number11
DOIs
Publication statusPublished - Nov 2005

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Cluster number
  • Clustering
  • Rival Penalization Controlled Competitive Learning
  • Stochastic RPCL

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