Model-based capacitated clustering with posterior regularization
| Autor: | Michael J. Fry, Feng Mai, Jeffrey W. Ohlmann |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
021103 operations research Information Systems and Management Optimization problem General Computer Science Computer science Heuristic Gaussian 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Mixture model 01 natural sciences Regularization (mathematics) Industrial and Manufacturing Engineering 010104 statistics & probability symbols.namesake Modeling and Simulation Expectation–maximization algorithm symbols Probability distribution 0101 mathematics Heuristics Cluster analysis |
| Zdroj: | European Journal of Operational Research. 271:594-605 |
| ISSN: | 0377-2217 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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