Model-based capacitated clustering with posterior regularization

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

European Journal of Operational Research, 2018, vol. 271, issue 2, 594-605

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.

Keywords: Heuristics; Gaussian mixture models; Expectation-maximization algorithm; Posterior regularization; Capacitated p-median problem (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:271:y:2018:i:2:p:594-605

DOI: 10.1016/j.ejor.2018.04.048

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