摘要：Block modeling has been used extensively in many domains including social science; spatial temporal data analysis and even medical imaging. Original formulations of the problem modeled it as a mixed integer programming problem; but were not scalable. Subsequent work relaxed the discrete optimization requirement; and showed that adding constraints is not straightforward in existing approaches. In this work; we present a new approach based on constraint programming; allowing discrete optimization of block modeling in a manner that is not only scalable; but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches; in both constrained and unconstrained settings; for an exhaustive search and for a type of local search called Large Neighborhood Search. We show its use in the analysis of real datasets. Finally; we show an application of the CP framework for model selection using the Minimum Description Length principle.
