.png){kind=icon}
Return
Model-based capacitated clustering with posterior regularization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2018.04.048.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Heuristics
Gaussian mixture models
Expectation-maximization algorithm
Posterior regularization
Capacitated p-median problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSaveShareSave View more