2012 DiscriminativeClusteringforMark

• (Haider et al., 2012) ⇒ Peter Haider, Luca Chiarandini, and Ulf Brefeld. (2012). “Discriminative Clustering for Market Segmentation.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339600

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Discriminative+Clustering+for+Market+Segmentation
• http://dl.acm.org/citation.cfm?id=2339530.2339600&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; discriminative clustering; market segmentation

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 DiscriminativeClusteringforMark	Peter Haider Luca Chiarandini Ulf Brefeld			Discriminative Clustering for Market Segmentation				10.1145/2339530.2339600		2012