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Autor*innen: Haider, Peter; Chiarandini, Luca; Brefeld, Ulf
Titel: Discriminative clustering for market segmentation
Aus: Association of Computational Linguistics (ACL) (Hrsg.): Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2012, New York: Association for Computing Machinery, 2012 , S. 417-425
DOI: 10.1145/2339530.2339600
URL: http://dl.acm.org/citation.cfm?id=2339530.2339600&coll=DL&dl=GUIDE&CFID=343017233&CFTOKEN=79756621
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Algorithmus; Computerunterstütztes Verfahren; Datenanalyse; Evaluation; Interaktion; Internet; Logdatei; Marktwirtschaft; Nutzerverhalten; Prognose; Suchmaschine
Abstract: 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.
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