A noise-resistant fuzzy c means algorithm for clustering

Krishna K. Chintalapudi, Moshe Kam

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

61 Scopus citations

Abstract

Probabilistic clustering techniques use the concept of memberships to describe the degree by which a vector belongs to a cluster. The use of memberships provides probabilistic methods with more realistic clustering than "hard" techniques. However, fuzzy schemes (like the fuzzy c means algorithm, FCM) are often sensitive to outliers. We review four existing algorithms, devised to reduce this sensitivity. These are: the noise cluster (NC) algorithm of Dave (1991), the possibilistic c means (PCM) scheme of Krishnapuram and Keller (1996), the least biased fuzzy clustering (LBFC) method of Beni and Liu (1994), and the fuzzy possibilistic c means algorithm of Pal et al. (1997). We then propose the new credibilistic fuzzy c means (CFCM) algorithm to improve on these methods. It uses a new variable, credibility of a vector, which measures the typicality of the vector to the whole data set. By taking credibility into account CFCM generates centroids which are less sensitive to outliers than other techniques, and closer to the centroids generated when the outliers are artificially removed.

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.
Pages1458-1463
Number of pages6
ISBN (Print)078034863X, 9780780348639
DOIs
StatePublished - Jan 1 1998
Externally publishedYes
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
CountryUnited 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

Fingerprint Dive into the research topics of 'A noise-resistant fuzzy c means algorithm for clustering'. Together they form a unique fingerprint.

Cite this