Two-Mode Cluster Analysis via Hierarchical Bayes

  • Wayne S. DeSarbo
  • Duncan K. H. Fong
  • John Liechty
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


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.


Markov Chain Monte Carlo Finite Mixture Proposal Distribution Full Conditional Distribution Posterior Odds 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Wayne S. DeSarbo
    • 1
  • Duncan K. H. Fong
    • 1
  • John Liechty
    • 1
  1. 1.Marketing Dept., Smeal College of BusinessPennsylvania State UniversityUniversity ParkUSA

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