Model based clustering of customer choice data

Donatella Vicari and Marco Alfó

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 3-13

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.

Keywords: Model-based clustering; Conditional logit; Multinomial logit; Co-clustering; Bi-clustering (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:3-13

DOI: 10.1016/j.csda.2013.09.014

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