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Gennclus: New Models for General Nonhierarchical Clustering Analysis

Published online by Cambridge University Press:  01 January 2025

Wayne S. Desarbo*
Affiliation:
Bell Laboratories
*
Requests for reprints should be sent to: Wayne S. DeSarbo, Bell Laboratories, Room 2C-479, 600 Mountain Avenue, Murray Hill, N.J., 07974.

Abstract

A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These (metric) clustering models generalize the Shepard-Arabie Additive Clusters model in allowing for: (1). either overlapping or nonoverlapping clusters; (2). either symmetric (one-way clustering) or nonsymmetric (two-way clustering) proximities (input data); and, (3). either symmetric or diagonal weights. The GENNCLUS algorithms utilize alternating least-squares methods combining ordinary and constrained least-squares, nonlinear constrained mathematical programming, and combinatorial optimization techniques in estimating model parameters. In addition to developing the mathematical bases of these models, a comprehensive set of Monte Carlo simulations of the different models is reported. Two applications concerning brand-switching data and celebrity-brand proximities are discussed. Finally, extensions to three-way models, nonmetric analyses, and other model specifications are provided.

Information

Type
Original Paper
Copyright
Copyright © 1982 The Psychometric Society

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