On SAT Models Enumeration in Itemset Mining
This work addresses the challenge of efficient enumeration in itemset mining for data analysis, but it appears incremental as it builds on existing SAT-based encodings.
The paper tackled the problem of improving models enumeration in SAT-based frequent itemset mining by adapting SAT solvers with strategies like restart, branching heuristics, and clause learning, resulting in an efficient SAT model enumerator as shown through experimental evaluation.
Frequent itemset mining is an essential part of data analysis and data mining. Recent works propose interesting SAT-based encodings for the problem of discovering frequent itemsets. Our aim in this work is to define strategies for adapting SAT solvers to such encodings in order to improve models enumeration. In this context, we deeply study the effects of restart, branching heuristics and clauses learning. We then conduct an experimental evaluation on SAT-Based itemset mining instances to show how SAT solvers can be adapted to obtain an efficient SAT model enumerator.