Fuzzy clustering validity for spatial data

Chunchun Hu, Lingkui Meng, Wen Zhong Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.
Original languageEnglish
Pages (from-to)191-196
Number of pages6
JournalGeo-Spatial Information Science
Volume11
Issue number3
DOIs
Publication statusPublished - 5 Sep 2008

Keywords

  • Fuzzy clustering
  • Spatial data
  • Uncertainty
  • Validity

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences

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