Probabilistic distance clustering: Algorithm and applications

C. Iyigun, A. Ben-Israel

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

The probabilistic distance clustering method of the authors [2, 8], assumes the cluster membership probabilities given in terms of the distances of the data points from the cluster centers, and the cluster sizes. A resulting extremal principle is then used to update the cluster centers (as convex combinations of the data points), and the cluster sizes (if not given.) Progress is monitored by the joint distance function (JDF), a weighted harmonic mean of the above distances, that approximates the data by capturing the data points in its lowest contours. The method is described, and applied to clustering, location problems, and mixtures of distributions, where it is a viable alternative to the Expectation-Maximization (EM) method. The JDF also helps to determine the "right" number of clusters for a given data set.

Original languageEnglish (US)
Title of host publicationClustering Challenges in Biological Networks
PublisherWorld Scientific Publishing Co.
Pages29-52
Number of pages24
ISBN (Electronic)9789812771667
ISBN (Print)9812771654, 9789812771650
DOIs
StatePublished - Jan 1 2009

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

Fingerprint

Dive into the research topics of 'Probabilistic distance clustering: Algorithm and applications'. Together they form a unique fingerprint.

Cite this