Clustering of relational data containing noise and outliers

Sumit Sen, Rajesh N. Davé

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Scopus citations

Abstract

The concept of noise clustering algorithm is applied to several fuzzy relational data clustering algorithms to make them more robust against noise and outliers. The methods considered include techniques proposed by Roubens (1978), Hathaway et al. (1994) and FANNY by Kaufman and Rouseeuw (1990). A new fuzzy relational data clustering (FRC) algorithm is proposed through generalization of FANNY. The FRC algorithm is shown to have the same objective functional as the relational fuzzy c-means algorithm. However, through use of direct objective function minimization based on the Lagrangian multiplier technique, the necessary conditions for minimization are derived without imposition of the restriction that the relational data is derived from Euclidean measure of distance from object data. Robustness of the new algorithm is demonstrated through several examples.

Original languageEnglish (US)
Title of host publication1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1411-1416
Number of pages6
ISBN (Print)078034863X, 9780780348639
DOIs
StatePublished - Jan 1 1998
Event1998 IEEE International Conference on Fuzzy Systems, FUZZY 1998 - Anchorage, United States
Duration: May 4 1998May 9 1998

Publication series

Name1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
Volume2

Other

Other1998 IEEE International Conference on Fuzzy Systems, FUZZY 1998
Country/TerritoryUnited States
CityAnchorage
Period5/4/985/9/98

All Science Journal Classification (ASJC) codes

  • Logic
  • Control and Optimization
  • Modeling and Simulation
  • Chemical Health and Safety
  • Software
  • Safety, Risk, Reliability and Quality

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