Discriminative clustering for market segmentation

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Authors

We study discriminative clustering for market segmentation tasks. The underlying problem setting resembles discriminative clustering, however, existing approaches focus on the prediction of univariate cluster labels. By contrast, market segments encode complex (future) behavior of the individuals which cannot be represented by a single variable. In this paper, we generalize discriminative clustering to structured and complex output variables that can be represented as graphical models. We devise two novel methods to jointly learn the classifier and the clustering using alternating optimization and collapsed inference, respectively. The two approaches jointly learn a discriminative segmentation of the input space and a generative output prediction model for each segment. We evaluate our methods on segmenting user navigation sequences from Yahoo! News. The proposed collapsed algorithm is observed to outperform baseline approaches such as mixture of experts. We showcase exemplary projections of the resulting segments to display the interpretability of the solutions.

Original languageEnglish
Title of host publicationProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Number of pages9
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Publication date2012
Pages417-425
ISBN (print)978-1-4503-1462-6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD-2012: Mining the Big Data - Beijing, China
Duration: 12.08.201216.08.2012
Conference number: 18
http://kdd2012.sigkdd.org/

    Research areas

  • Informatics - Alternating optimizations, Discriminative clustering , GraphicaL model, Input space, Interpretability, Market segment, Market segmentation, Mixture of experts, Novel methods, Output variables, Prediction model, Single variable, Univariate, User navigation
  • Business informatics

DOI

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