Probabilistic distance clustering adjusted for cluster size

Cem Iyigun, Adi Ben-Israel

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The probabilistic distance clustering method of works well if the cluster sizes are approximately equal. We modify that method to deal with clusters of arbitrary size and for problems where the cluster sizes are themselves unknowns that need to be estimated. In the latter case, our method is a viable alternative to the estimating multinomial (EM) method.

Original languageEnglish (US)
Pages (from-to)603-621
Number of pages19
JournalProbability in the Engineering and Informational Sciences
Volume22
Issue number4
DOIs
StatePublished - Oct 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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