Multiassignment clustering for Boolean data
Mario Frank, Andreas P. Streich, Joachim M. Buhmann · 2009
We propose a probabilistic model for clustering Boolean data where an object can be simultaneously assigned to multiple clusters. By explicitly modeling the underlying gen-erative process that combines the individual source emissions, highly structured data are expressed with substantially fewer clusters compared to single-assignment clustering. As a consequence, such a model provides robust parameter estimators even when the number of samples is low. We extend the model with different noise processes and demonstrate that maximum-likelihood estimation with multiple assignments consistently infers source parameters more accurately than single-assignment clustering. Our model is primarily mo-tivated by the task of role mining for role-based access control, where users of a system are assigned one or more roles. In experiments with real-world access-control data, our model exhibits better generalization performance than state-of-the-art approaches.