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ERIC Number: EJ789904
Record Type: Journal
Publication Date: 2008-Mar
Pages: 17
Abstractor: Author
Reference Count: 0
ISSN: ISSN-0033-3123
Optimal Partitioning of a Data Set Based on the "p"-Median Model
Brusco, Michael J.; Kohn, Hans-Friedrich
Psychometrika, v73 n1 p89-105 Mar 2008
Although the "K"-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The "p"-median model is an especially well-studied clustering problem that requires the selection of "p" objects to serve as cluster centers. The objective is to choose the cluster centers such that the sum of the Euclidean distances (or some other dissimilarity measure) of objects assigned to each center is minimized. Using 12 data sets from the literature, we demonstrate that a three-stage procedure consisting of a greedy heuristic, Lagrangian relaxation, and a branch-and-bound algorithm can produce globally optimal solutions for "p"-median problems of nontrivial size (several hundred objects, five or more variables, and up to 10 clusters). We also report the results of an application of the "p"-median model to an empirical data set from the telecommunications industry.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: Researchers
Language: English
Sponsor: N/A
Authoring Institution: N/A