Robust clustering methods: A unified view

Rajesh N. Davé, Raghu Krishnapuram

Research output: Contribution to journalReview articlepeer-review

633 Scopus citations


Clustering methods need to be robust if they are to be useful in practice. In this paper, we analyze several popular robust clustering methods and show that they have much in common. We also establish a connection between fuzzy set theory and robust statistics and point out the similarities between robust clustering methods and statistical methods such as the weighted least-squares (LS) technique, the M estimator, the minimum volume ellipsoid (MVE) algorithm, cooperative robust estimation (CRE), minimization of probability of randomness (MINPRAN), and the epsilon contamination model. By gleaning the common principles upon which the methods proposed in the literature are based, we arrive at a unified view of robust clustering methods. We define several general concepts that are useful in robust clustering, state the robust clustering problem in terms of the defined concepts, and propose generic algorithms and guidelines for clustering noisy data. We also discuss why the generalized Hough transform is a suboptimal solution to the robust clustering problem.

Original languageEnglish (US)
Pages (from-to)270-293
Number of pages24
JournalIEEE Transactions on Fuzzy Systems
Issue number2
StatePublished - 1997

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


  • Clustering validity
  • Fuzzy clustering
  • Robust methods


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