Rival penalization controlled competitive learning for data clustering with unknown cluster number

Yiu Ming CHEUNG*

*Corresponding author for this work

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

34 Citations (Scopus)

Abstract

Conventional clustering algorithms such as k-means (Forgy 1965, MacQueen 1967) need to know the exact cluster number k∗ before performing data clustering. Otherwise, they will lead to a poor clustering performance. Unfortunately, it is often hard to determine k∗ in advance in many practical problems. Under the circumstances, Xu et al. in 1993 proposed an approach named Rival Penalized Competitive Learning (RPCL) algorithm that can perform appropriate clustering without knowing the cluster number by automatically driving extra seed points far away from the input data set. Although RPCL has made great success in many applications, its performance is however very sensitive to the selection of the de-learning rate. To our best knowledge, there is still an open problem to guide this rate selection. We further investigate RPCL by presenting a mechanism to dynamically control the rival-penalizing forces. Consequently, we give out a rival penalized controlled competitive learning (RPCCL) approach, which circumvents the selecting problem of the de-learning rate by always fixing it at the same value as the learning rate. In contrast, the RPCL cannot do that in the same way. The experiments have shown the outstanding performance of this algorithm in comparison with the RPCL.

Original languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
Subtitle of host publicationComputational Intelligence for the E-Age
EditorsJagath C. Rajapakse, Xin Yao, Lipo Wang, Kunihiko Fukushima, Soo-Young Lee
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages467-471
Number of pages5
ISBN (Electronic)9810475241, 9789810475246
DOIs
Publication statusPublished - 2002
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 18 Nov 200222 Nov 2002

Publication series

NameICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
Volume1

Conference

Conference9th International Conference on Neural Information Processing, ICONIP 2002
Country/TerritorySingapore
CitySingapore
Period18/11/0222/11/02

Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Fingerprint

Dive into the research topics of 'Rival penalization controlled competitive learning for data clustering with unknown cluster number'. Together they form a unique fingerprint.

Cite this