Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Mining generalised disjunctive association rules
Proceedings of the tenth international conference on Information and knowledge management
Discovering All Most Specific Sentences by Randomized Algorithms
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Reasoning about sets using redescription mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Microarray data mining with visual programming
Bioinformatics
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
BLOSOM: a framework for mining arbitrary boolean expressions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns
Statistical Analysis and Data Mining
Approximating the number of frequent sets in dense data
Knowledge and Information Systems
Output space sampling for graph patterns
Proceedings of the VLDB Endowment
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Direct local pattern sampling by efficient two-step random procedures
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining succinct systems of minimal generators of formal concepts
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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We tackle the challenging problem of mining the simplest Boolean patterns from categorical datasets. Instead of complete enumeration, which is typically infeasible for this class of patterns, we develop effective sampling methods to extract a representative subset of the minimal Boolean patterns (in disjunctive normal form - DNF). We make both theoretical and practical contributions, which allow us to prune the search space based on provable properties. Our approach can provide a near-uniform sample of the minimal DNF patterns. We also show that the mined minimal DNF patterns are very effective when used as features for classification.