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
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On Closed Constrained Frequent Pattern Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Pattern lattice traversal by selective jumps
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Parallel Bifold: Large-scale parallel pattern mining with constraints
Distributed and Parallel Databases
A proximate dynamics model for data mining
Expert Systems with Applications: An International Journal
Mining constraint-based patterns using automatic relaxation
Intelligent Data Analysis
On the Complexity of Constraint-Based Theory Extraction
DS '09 Proceedings of the 12th International Conference on Discovery Science
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Mining for frequent itemsets can generate an overwhelming number of patterns, often exceeding the size of the original transactional database. One way to deal with this issue is to set filters and interestingness measures. Others advocate the use of constraints to apply to the patterns, either on the form of the patterns or on descriptors of the items in the patterns. However, typically the filtering of patterns based on these constraints is done as a post-processing phase. Filtering the patterns post-mining adds a significant overhead, still suffers from the sheer size of the pattern set and loses the opportunity to exploit those constraints. In this paper we propose an approach that allows the efficientmining of frequent itemsets patterns, while pushing simultaneously both monotone and anti-monotone constraints during and at different strategic stages of the mining process. Our implementation shows a significant improvement when considering the constraints early and a better performance over Dualminer which also considers both types of constraints.