NDLI: Clustering Qualitative Data Based on Binary Equivalence Relations: Neighborhood Search Heuristics for the Clique Partitioning Problem

Content Provider	Springer Nature Link
Author	Brusco, Michael J. Köhn, Hans Friedrich
Copyright Year	2009
Abstract	The clique partitioning problem (CPP) requires the establishment of an equivalence relation for the vertices of a graph such that the sum of the edge costs associated with the relation is minimized. The CPP has important applications for the social sciences because it provides a framework for clustering objects measured on a collection of nominal or ordinal attributes. In such instances, the CPP incorporates edge costs obtained from an aggregation of binary equivalence relations among the attributes. We review existing theory and methods for the CPP and propose two versions of a new neighborhood search algorithm for efficient solution. The first version (NS-R) uses a relocation algorithm in the search for improved solutions, whereas the second (NS-TS) uses an embedded tabu search routine. The new algorithms are compared to simulated annealing (SA) and tabu search (TS) algorithms from the CPP literature. Although the heuristics yielded comparable results for some test problems, the neighborhood search algorithms generally yielded the best performances for large and difficult instances of the CPP.
Starting Page	685
Ending Page	703
Page Count	19
File Format	PDF
ISSN	00333123
Journal	Psychometrika
Volume Number	74
Issue Number	4
e-ISSN	18600980
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-04-28
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	equivalence relation clique partitioning clustering heuristics tabu search simulated annealing neighborhood search Statistical Theory and Methods Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Assessment, Testing and Evaluation Psychometrics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Psychology

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