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
Date Written: 2003
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.
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