Generic Constraint-based Block Modeling using Constraint Programming

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 discrete optimization requirement, showed that adding constraints is straightforward existing approaches. In this work, we present new approach based on constraint programming, allowing block manner only scalable, also allows easy incorporation constraints. We introduce filtering algorithm outperforms earlier approaches, both constrained unconstrained settings, for an exhaustive search type local called Large Neighborhood Search. show its use real datasets. Finally, application CP framework model selection using Minimum Description Length principle.