A new feature weighted fuzzy C-means clustering algorithm

Huaiguo Fu, Ahmed M. Elmisery

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

In the field of cluster analysis, most of existing algorithms assume that each feature of the samples plays a uniform contribution for cluster analysis. Feature-weight assignment is a special case of feature selection where different features are ranked according to their importance. The feature is assigned a value in the interval [0, 1] indicating the importance of that feature, we call this value "feature-weight". In this paper we propose a new feature weighted fuzzy c-means clustering algorithm in a way which this algorithm be able to obtain the importance of each feature, and then use it in appropriate assignment of feature-weight. These weights incorporated into the distance measure to shape clusters based on variability, correlation and weighted features.

Original languageEnglish
Title of host publicationProceedings of the IADIS European Conference on Data Mining 2009, ECDM'09 Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009
Pages11-18
Number of pages8
Publication statusPublished - 2009
EventIADIS European Conference on Data Mining 2009, ECDM'09. Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009 - Algarve, Portugal
Duration: 18 Jun 200920 Jun 2009

Publication series

NameProceedings of the IADIS European Conference on Data Mining 2009, ECDM'09 Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009

Conference

ConferenceIADIS European Conference on Data Mining 2009, ECDM'09. Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009
Country/TerritoryPortugal
CityAlgarve
Period18/06/200920/06/2009

Keywords

  • Cluster analysis
  • Feature weighted
  • Fuzzy clustering

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