Decomposition Based SAT Encodings for Itemset Mining Problems

Abstract

Recently, several constraint programming (CP)/propositional satisfiability (SAT) based encodings have been proposed to deal with various data mining problems including itemset and sequence mining problems. This research issue allows to model data mining problems in a declarative way, while exploiting efficient and generic solving techniques. In practice, for large datasets, they usually lead to constraints network/Boolean formulas of huge size. Space complexity is clearly identified as the main bottleneck behind the competitiveness of these new declarative and flexible models w.r.t. specialized data mining approaches. In this paper, we address this issue by considering SAT based encodings of itemset mining problems. By partitioning the transaction database, we propose a new encoding framework for SAT based itemset mining problems. Experimental results on several known datasets show significant improvements, up to several orders of magnitude.