A parallel branch‐and‐bound methodfor cluster analysis

L.S. Iyer and J.E. Aronson

Annals of Operations Research, 1999, vol. 90, issue 0, 65-86

Abstract: Cluster analysis is a generic term coined for procedures that are used objectively to groupentities based on their similarities and differences. The primary objective of these proceduresis to group n items into K mutually exclusive clusters so that items within each cluster arerelatively homogeneous in nature while the clusters themselves are distinct. In this research,we have developed, implemented and tested an asynchronous, dynamic parallel branch‐and‐bound algorithm to solve the clustering problem. In the developmental environment,several processes (tasks) work independently on various subproblems generated by thebranch‐and‐bound procedure. This parallel algorithm can solve very large‐scale, optimalclustering problems in a reasonable amount of wall‐clock time. Linear and superlinear speed‐upsare obtained. Thus, solutions to real‐world, complex clustering problems, which couldnot be solved due to the lack of efficient parallel algorithms, can now be attempted. Copyright Kluwer Academic Publishers 1999

Date: 1999
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DOI: 10.1023/A:1018925018009

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