Author:
Iyigun Cem,Ben-Israel Adi
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 expectation-maximization (EM) method.
Publisher
Cambridge University Press (CUP)
Subject
Industrial and Manufacturing Engineering,Management Science and Operations Research,Statistics, Probability and Uncertainty,Statistics and Probability
Reference12 articles.
1. Probabilistic D-Clustering
2. Sur le point par lequel la somme des distances de n points donnés est minimum;Weiszfeld;Tohoku Mathematics Journal,1937
Cited by
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