Model-based capacitated clustering with posterior regularization

Feng Mai, Michael J. Fry, Jeffrey W. Ohlmann

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

We propose a heuristic approach to address the general class of optimization problems involving the capacitated clustering of observations consisting of variable values that are realizations from respective probability distributions. Based on the expectation-maximization algorithm, our approach unifies Gaussian mixture modeling for clustering analysis and cluster capacity constraints using a posterior regularization framework. To test our algorithm, we consider the capacitated p-median problem in which the observations consist of geographic locations of customers and the corresponding demand of these customers. Our heuristic has superior performance compared to classic geometrical clustering heuristics, with robust performance over a collection of instance types.

Original languageEnglish
Pages (from-to)594-605
Number of pages12
JournalEuropean Journal of Operational Research
Volume271
Issue number2
DOIs
StatePublished - 1 Dec 2018

Keywords

  • Capacitated p-median problem
  • Expectation-maximization algorithm
  • Gaussian mixture models
  • Heuristics
  • Posterior regularization

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