GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
A linear-time transformation of linear inequalities into conjunctive normal form
Information Processing Letters
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
A machine program for theorem-proving
Communications of the ACM
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Output-Sensitive Flexible Pattern Discovery Algorithm
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
Bases of Motifs for Generating Repeated Patterns with Wild Cards
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Prism: A Primal-Encoding Approach for Frequent Sequence Mining
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
Clasp: a conflict-driven answer set solver
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Itemset mining: A constraint programming perspective
Artificial Intelligence
A Constraint Programming Approach for Enumerating Motifs in a Sequence
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
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In this paper, we propose a SAT-based encoding for the problem of discovering frequent, closed and maximal patterns in a sequence of items and a sequence of itemsets. Our encoding can be seen as an improvement of the approach proposed in [8] for the sequences of items. In this case, we show experimentally on real world data that our encoding is significantly better. Then we introduce a new extension of the problem to enumerate patterns in a sequence of itemsets. Thanks to the flexibility and to the declarative aspects of our SAT-based approach, an encoding for the sequences of itemsets is obtained by a very slight modification of that for the sequences of items.