A fast method for discovering suitable number of clusters for fuzzy clustering

Ping Yu Hsu, Phan Anh Huy Nguyen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

One main problem of Fuzzy c-Means (FCM) is deciding on an appropriate number of clusters. Although methods have been proposed to address this, they all require clustering algorithms to be executed several times before the right number is chosen. The aim of this study was to develop a method for determining cluster numbers without repeated execution. We propose a new method that combines FCM and singular value decomposition. Based on the percentage of variance, this method can calculate the appropriate number of clusters. The proposed method was applied to several well-known datasets to demonstrate its effectiveness.

Original languageEnglish
Pages (from-to)1523-1538
Number of pages16
JournalIntelligent Data Analysis
Volume26
Issue number6
DOIs
StatePublished - 2022

Keywords

  • Fuzzy c-Means (FCM)
  • clustering
  • number of clusters
  • processing time
  • singular value decomposition

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