Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Frequent-Pattern based Iterative Projected Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Optimization of association rule mining queries
Intelligent Data Analysis
On the Complexity of Constraint-Based Theory Extraction
DS '09 Proceedings of the 12th International Conference on Discovery Science
Software—Practice & Experience
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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ExAnte is a simple yet effective approach for preprocessing input data for mining frequent patterns. The approach questions established research in that it requires no trade-off between antimonotonicity and monotonicity. Indeed, ExAnte relies on a strong synergy between these two opposite components and exploits it to dramatically reduce the data being analyzed to that containing interesting patterns. This data reduction, in turn, induces a strong reduction of the candidate patterns' search space. The result is significant performance improvements in subsequent mining. It can also make feasible some otherwise intractable mining tasks. The authors describe their technology and experiments that proved its effectiveness using different constraints on various data sets.