Discriminative clustering for market segmentation

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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.
OriginalspracheEnglisch
TitelProceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Anzahl der Seiten9
ErscheinungsortNew York
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum2012
Seiten417-425
ISBN (Print)978-1-4503-1462-6
DOIs
PublikationsstatusErschienen - 2012
Extern publiziertJa
Veranstaltung18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD-2012: Mining the Big Data - Beijing, China
Dauer: 12.08.201216.08.2012
Konferenznummer: 18
http://kdd2012.sigkdd.org/

DOI