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Model based clustering of customer choice data

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  • Vicari, Donatella
  • Alfó, Marco

Abstract

In several empirical applications analyzing customer-by-product choice data, it may be relevant to partition individuals having similar purchase behavior in homogeneous segments. Moreover, should individual- and/or product-specific covariates be available, their potential effects on the probability to choose certain products may be also investigated. A model for joint clustering of statistical units (customers) and variables (products) is proposed in a mixture modeling framework, and an appropriate EM-type algorithm for ML parameter estimation is presented. The model can be easily linked with similar proposals appeared in various contexts, such as co-clustering of gene expression data, clustering of words and documents in web-mining data analysis.

Suggested Citation

  • Vicari, Donatella & Alfó, Marco, 2014. "Model based clustering of customer choice data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 3-13.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:3-13
    DOI: 10.1016/j.csda.2013.09.014
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    References listed on IDEAS

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    1. Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    2. Li, Jia & Zha, Hongyuan, 2006. "Two-way Poisson mixture models for simultaneous document classification and word clustering," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 163-180, January.
    3. Martella Francesca & Alfò Marco & Vichi Maurizio, 2008. "Biclustering of Gene Expression Data by an Extension of Mixtures of Factor Analyzers," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-21, February.
    4. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, January.
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