A Structure-preserving Clause Form Translation
Journal of Symbolic Computation
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Discovering all most specific sentences
ACM Transactions on Database Systems (TODS)
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Compact Representation of Sets of Binary Constraints
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Implementing a generalized version of resolution
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Itemset mining: A constraint programming perspective
Artificial Intelligence
Automatic extraction of functional dependencies
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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In this paper, we propose a first application of data mining techniques to propositional satisfiability. Our proposed mining based compression approach aims to discover and to exploit hidden structural knowledge for reducing the size of propositional formulae in conjunctive normal form (CNF). It combines both frequent itemset mining techniques and Tseitin's encoding for a compact representation of CNF formulae. The experimental evaluation of our approach shows interesting reductions of the sizes of many application instances taken from the last SAT competitions.