Cooperative and penalized competitive learning with application to kernel-based clustering

Hong Jia, Yiu Ming CHEUNG*, Jiming LIU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

23 Citations (Scopus)


Competitive learning approaches with individual penalization or cooperation mechanisms have the attractive ability of automatic cluster number selection in unsupervised data clustering. In this paper, we further study these two mechanisms 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 locate the cluster centers more quickly and be insensitive to the number of seed points and their initial positions. Additionally, to handle nonlinearly separable clusters, we further introduce the proposed competition mechanism into kernel clustering framework. Correspondingly, a new kernel-based competitive learning algorithm which can conduct nonlinear partition without knowing the true cluster number is presented. The promising experimental results on real data sets demonstrate the superiority of the proposed methods.

Original languageEnglish
Pages (from-to)3060-3069
Number of pages10
JournalPattern Recognition
Issue number9
Publication statusPublished - Sept 2014

Scopus Subject Areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Competitive learning
  • Cooperation mechanism
  • Kernel-based clustering
  • Number of clusters
  • Penalization mechanism


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