TY - JOUR
T1 - Model-based capacitated clustering with posterior regularization
AU - Mai, Feng
AU - Fry, Michael J.
AU - Ohlmann, Jeffrey W.
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - Capacitated p-median problem
KW - Expectation-maximization algorithm
KW - Gaussian mixture models
KW - Heuristics
KW - Posterior regularization
UR - http://www.scopus.com/inward/record.url?scp=85048976422&partnerID=8YFLogxK
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U2 - 10.1016/j.ejor.2018.04.048
DO - 10.1016/j.ejor.2018.04.048
M3 - Article
AN - SCOPUS:85048976422
SN - 0377-2217
VL - 271
SP - 594
EP - 605
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 2
ER -