Probabilistic distance clustering adjusted for cluster size

Cem Iyigun; Adi Ben-Israel

doi:10.1017/S0269964808000351

Probabilistic distance clustering adjusted for cluster size

Cem Iyigun, Adi Ben-Israel

Mgmt Science & Info Systems

Research output: Contribution to journal › Article › peer-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 language	English (US)
Pages (from-to)	603-621
Number of pages	19
Journal	Probability in the Engineering and Informational Sciences
Volume	22
Issue number	4
DOIs	https://doi.org/10.1017/S0269964808000351
State	Published - 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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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.",

author = "Cem Iyigun and Adi Ben-Israel",

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AB - 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.

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Probabilistic distance clustering adjusted for cluster size

Abstract

All Science Journal Classification (ASJC) codes

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