Double indices induced FCM clustering and its integration with fuzzy subspace clustering

Jun Wang, Shitong Wang, Zhaohong Deng, Fu Lai Korris Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Fuzzy c-means is one of the most popular algorithms for clustering analysis. In this study, a novel FCM based algorithm called double indices induced FCM (DI-FCM) is developed from a new perspective. DI-FCM introduces a power exponent r into the constraints of the objective function such that the range of the fuzziness index m is extended. Furthermore, it can be explained from the perspective of entropy concept that the power exponent r facilitates the introduction of entropy based constraints into fuzzy clustering algorithms. As an attractive and judicious application, DI-FCM is integrated with the fuzzy subspace clustering (FSC) algorithm so that a novel subspace clustering algorithm called double indices induced fuzzy subspace clustering (DI-FSC) algorithm is proposed for high dimensional data. In DI-FSC, the commonly-used Euclidean distance is replaced by the feature-weighted distance, which results in two fuzzy matrices in the objective function. Meanwhile, the convergence property of DI-FSC is also investigated. Experiments on the artificial data as well as the real text data were conducted and the experimental results show the effectiveness of the proposed algorithm.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
DOIs
Publication statusPublished - 23 Oct 2012
Event2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Conference

Conference2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • feature weighting
  • fuzzy clustering
  • fuzzy subspace clustering
  • text clustering

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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