Two-Mode Cluster Analysis via Hierarchical Bayes

Studies in Classification, Data Analysis, and Knowledge Organization, Chapter: Innovations in Classification, Data Science, and Information Systems, pp 19-29, 2003

11 Pages Posted: 11 Jun 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Duncan K. H. Fong

Pennsylvania State University

John Liechty

Pennsylvania State University, University Park

Date Written: 2003

Abstract

This manuscript introduces a new Bayesian finite mixture methodology for the joint clustering of row and column stimuli/objects associated with two-mode asymmetric proximity, dominance, or profile data. That is, common clusters are derived which partition both the row and column stimuli/objects simultaneously into the same derived set of clusters. In this manner, interrelationships between both sets of entities (rows and columns) are easily ascertained. We describe the technical details of the proposed two-mode clustering methodology including its Bayesian mixture formulation and a Bayes factor heuristic for model selection. Lastly, a marketing application is provided examining consumer preferences for various brands of luxury automobiles.

Suggested Citation

DeSarbo, Wayne S. and Fong, Duncan K. H. and Liechty, John, Two-Mode Cluster Analysis via Hierarchical Bayes (2003). Studies in Classification, Data Analysis, and Knowledge Organization, Chapter: Innovations in Classification, Data Science, and Information Systems, pp 19-29, 2003, Available at SSRN: https://ssrn.com/abstract=2793126

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Duncan K. H. Fong

Pennsylvania State University ( email )

308 armsby
university park, PA 16802
United States

John Liechty

Pennsylvania State University, University Park ( email )

University Park
State College, PA 16802
United States

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