On robustifying the C-means algorithms

Jongwoo Kim, Raghu Krishnapuram, Rajesh Dave

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

The Hard and Fuzzy C-Means algorithms are commonly used in many applications. However, they are highly sensitive to noise and outliers. In this paper, we reformulate the Hard and Fuzzy C-Means algorithms and combine them with a robust estimator called the Least Trimmed Squares to produce robust versions of these algorithms. To find the optimum trimming ratio of the data set and to eliminate the noise from the data set, we develop an unsupervised algorithm based on a cluster validity measure. We illustrate the robustness of these algorithm with examples.

Original languageEnglish (US)
Pages630-635
Number of pages6
StatePublished - Dec 1 1995
Externally publishedYes
EventProceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, (ISUMA - NAFIPS'95) - College Park, MD, USA
Duration: Sep 17 1995Sep 20 1995

Other

OtherProceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, (ISUMA - NAFIPS'95)
CityCollege Park, MD, USA
Period9/17/959/20/95

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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