Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Discriminative Clustering: Optimal Contingency Tables by Learning Metrics
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
An extended self-organizing map network for market segmentation: a telecommunication example
Decision Support Systems
Adaptive mixtures of local experts
Neural Computation
Efficient multiclass maximum margin clustering
Proceedings of the 25th international conference on Machine learning
Maximum Margin Clustering with Multivariate Loss Function
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
A Two Phase Clustering Method for Intelligent Customer Segmentation
ISMS '10 Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation
Least squares quantization in PCM
IEEE Transactions on Information Theory
Is data clustering in adversarial settings secure?
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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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.