Generic Constraint-based Block Modeling using Constraint Programming
(2019) Constraints : an international journal — Vol. 25, n° 1, p. ?? (2019)
Files
main.pdf
preprint
Open Access - Adobe PDF
- 960.14 KB
Details
- Authors
- Abstract
- 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 the problem 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. We show its use in the analysis of real datasets.
- Affiliations
University of California at Davis Computer Scienc Department
Citations
Alex Mattenet, Ian Davidson, Nijssen, S., & Schaus, P. (2019). Generic Constraint-based Block Modeling using Constraint Programming. Constraints : an international journal, 25(1), ?? https://hdl.handle.net/2078.5/126743 (Original work published 2019)