Application of the least trimmed squares technique to prototype-based clustering

Jongwoo Kim, Raghu Krishnapuram, Rajesh Davé

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Prototype-based clustering algorithms such as the K-means and the Fuzzy C-Means algorithms are sensitive to noise and outliers. This paper shows how the Least Trimmed Squares technique can be incorporated into prototype-based clustering algorithms to make them robust.

Original languageEnglish (US)
Pages (from-to)633-641
Number of pages9
JournalPattern Recognition Letters
Volume17
Issue number6
DOIs
StatePublished - May 15 1996

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Fuzzy C-means
  • K-means
  • Least trimmed squares
  • Noisy data
  • Robust clustering

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