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Pairwise Partitioning: A Nonmetric Algorithm for Identifying Feature-Based Similarity Structures

Published online by Cambridge University Press:  01 January 2025

J. Wesley Hutchinson  and
Amitabh Mungale
J. Wesley Hutchinson*
Affiliation:
University of Florida University of Pennsylvania
Amitabh Mungale
Affiliation:
Rutgers University
*
Requests for reprints should be sent to Wes Hutchinson, Marketing Department, 1457 SHDH, University of Pennsylvania, Philadelphia, PA 19104.
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Abstract

Pairwise partitioning is a nonmetric, divisive algorithm, for identifying feature structures based on pairwise similarities. For errorless data, this algorithm is shown to identify only (and sometimes all) valid features for certain hierarchical and multidimensional feature structures. Unfortunately, the algorithm is also extremely sensitive to error in the data. Fortunately, several modifications of the algorithm are shown to compensate significantly for this deficiency. The algorithm is illustrated with simulations and analyses of three sets of similarity data. The results suggest that this algorithm will be most useful (a) as an exploratory tool for generating a relatively large set of potential features that can be reduced using other criteria (either statistical or substantive) and (b) as a source of confirmatory or disconfirmatory evidence.

Keywords

additive treescluster analysisfeature structureslinear arraysoverlapping clusteringpartitioningsimilarity

Information

Type
Original Paper
Information
Psychometrika , Volume 62 , Issue 1 , March 1997 , pp. 85 - 117
Copyright
Copyright © 1997 The Psychometric Society

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Footnotes

This research has been partially supported by funding from the College of Business and the Center for Retailing Education and Research at the University of Florida. We are also grateful for the helpful comments of Phipps Arabie, two anonymous reviewers, the associate editor, and editor on earlier versions of the manuscript.

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