Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

Alberto Lumbreras (), Julien Velcin (), Marie Guégan () and Bertrand Jouve ()
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Alberto Lumbreras: Technicolor
Julien Velcin: Université de Lyon
Marie Guégan: Technicolor
Bertrand Jouve: Université de Toulouse

Computational Statistics, 2017, vol. 32, issue 1, No 7, 145-177

Abstract: Abstract We present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to infer the groups of users as well as their latent behavioral functions. We also propose a non-parametric version based on a Dirichlet Process to automatically infer the number of clusters. We test the properties and performance of the model on a synthetic dataset that represents the participation of users in the threads of an online forum. Experiments show that dual-view models outperform single-view ones when one of the views lacks information.

Keywords: Multi-view clustering; Model-based clustering; Dirichlet Process (DP); Chinese Restaurant Process (CRP) (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-016-0668-0

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