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.
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© 2005 Springer-Verlag Berlin · Heidelberg
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DeSarbo, W.S., Fong, D.K.H., Liechty, J. (2005). Two-Mode Cluster Analysis via Hierarchical Bayes. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_3
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DOI: https://doi.org/10.1007/3-540-26981-9_3
Publisher Name: Springer, Berlin, Heidelberg
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