Gennclus: New models for general nonhierarchical clustering analysis

Wayne Desarbo

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)449-475
Number of pages27
JournalPsychometrika
Volume47
Issue number4
DOIs
StatePublished - Dec 1 1982

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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