Visual sampling based clustering algorithm VSC

Wang Shitong, Fu Lai Korris Chung, Guo Wei, Han Bin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

This study attempts to achieve two goals: (1) The novel visual sampling based clustering algorithm VSC is proposed, based on the visual sampling principle. The clustering algorithm VSC incorporates the visual sampling principle together with the famous Weber law such that it has two distinctive advantages: (a) it is insensitive to initial conditions and very effective for convex datasets; (b) the reasonable cluster number can be effectively determined by the new Weber-law-based clustering validity index. Our experimental results demonstrate its success. (2) The link relationship between our algorithm VSC and algorithm SCA. Both theoretical analysis and experimental results show that in many cases, our algorithm VSC here has almost the same clustering results as algorithm SCA. This fact reveals that our algorithm can be utilized to overcome the drawback of SCA, i.e., the parameter γ therein is very difficult to be well determined.
Original languageEnglish
Pages (from-to)779-791
Number of pages13
JournalInformation Technology Journal
Volume5
Issue number5
DOIs
Publication statusPublished - 1 Sep 2006

Keywords

  • Attractors
  • Clustering
  • Clustering validity index
  • Fixed points
  • Visual sampling
  • Weber law

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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