Robust fuzzy clustering algorithms

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

63 Scopus citations


A class of fuzzy clustering algorithms based on a recently introduced 'noise cluster' concepts is proposed. A 'noise prototype' is defined such that it is equi-distant to all the points in the data-set. This allows for detection of clusters amongst data with or without noise. It is shown that this concept is applicable to all the generalizations of fuzzy or hard k-means algorithms. Various applications are also considered. Application of this concept to a variety of regression problems is also considered. It is shown that the results of this approach are comparable to many robust regression techniques. The paper concludes with a summary and directions for future work.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Fuzzy Systems
PublisherPubl by IEEE
Number of pages6
ISBN (Print)0780306155
StatePublished - 1993
EventSecond IEEE International Conference on Fuzzy Systems - San Francisco, CA, USA
Duration: Mar 28 1993Apr 1 1993

Publication series

Name1993 IEEE International Conference on Fuzzy Systems


OtherSecond IEEE International Conference on Fuzzy Systems
CitySan Francisco, CA, USA

All Science Journal Classification (ASJC) codes

  • General Engineering


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