Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Learning correlations using the mixture-of-subsets model
ACM Transactions on Knowledge Discovery from Data (TKDD)
Statistical models for partial membership
Proceedings of the 25th international conference on Machine learning
Multi-view clustering using mixture models in subspace projections
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
RolX: structural role extraction & mining in large graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A global local modeling of internet usage in large mobile societies
Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
Guided learning for role discovery (GLRD): framework, algorithms, and applications
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Archived data often describe entities that participate in multiple roles. Each of these roles may influence various aspects of the data. For example, a register transaction collected at a retail store may have been initiated by a person who is a woman, a mother, an avid reader, and an action movie fan. Each of these roles can influence various aspects of the customer's purchase: the fact that the customer is a mother may greatly influence the purchase of a toddler-sized pair of pants, but have no influence on the purchase of an action-adventure novel. The fact that the customer is an action move fan and an avid reader may influence the purchase of the novel, but will have no effect on the purchase of a shirt. In this paper, we present a generic, Bayesian framework for capturing exactly this situation. In our framework, it is assumed that multiple roles exist, and each data point corresponds to an entity (such as a retail customer, or an email, or a news article) that selects various roles which compete to influence the various attributes associated with the data point. We develop robust, MCMC algorithms for learning the models under the framework.