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Bayesian estimation of membership uncertainty in model‐based clustering
Author(s) -
Chen Liyuan,
Brown Steven D.
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2511
Subject(s) - cluster analysis , single linkage clustering , hierarchical clustering , computer science , correlation clustering , fuzzy clustering , cure data clustering algorithm , determining the number of clusters in a data set , data mining , markov chain monte carlo , gibbs sampling , mixture model , consensus clustering , artificial intelligence , pattern recognition (psychology) , mathematics , bayesian probability
We report the use of a cluster analysis method based on a multivariate mixture model, known as model‐based clustering, for overcoming the limitations of hierarchical clustering and relocation clustering. Unlike traditional clustering methods in which clusters are formed on the basis of intercluster distances, model‐based clustering classifies observations on the basis of probability estimated from Gaussian mixture modeling, and its statistical basis allows for inference. Three examples are given in which we demonstrate that model‐based clustering gives much better performance for overlapping clusters, a more reliable determination of the number of clusters in data, and better identification of clustering in the presence of outliers than agglomerative hierarchical clustering or iterative relocation clustering using a K ‐means criterion. We also show that Markov chain Monte Carlo simulation, as implemented via Gibbs sampling coupled with model‐based clustering, may be used to assess uncertainty of group memberships. Copyright © 2013 John Wiley & Sons, Ltd.