Probabilistic distance clustering adjusted for cluster size

Authors:
Cem Iyigun;Adi Ben-israel
Affiliations:
Rutgers center for operations research, and department of management science and information systems, school of business, rutgers university e-mail: iyigun@business.rutgers.edu;Rutgers center for operations research, and department of management science and information systems, school of business, rutgers university e-mail: iyigun@business.rutgers.edu
Venue:
Probability in the Engineering and Informational Sciences
Year:
2008

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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.