Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints
Data Mining and Knowledge Discovery
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Closed Pattern Mining in Strongly Accessible Set Systems (Extended Abstract)
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Efficient constraint propagation engines
ACM Transactions on Programming Languages and Systems (TOPLAS)
A constraint-based querying system for exploratory pattern discovery
Information Systems
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Itemset mining: A constraint programming perspective
Artificial Intelligence
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Over the years many pattern mining tasks and algorithms have been proposed. Traditionally, the focus of these studies was on the efficiency of the computation and the scalability towards very large databases. Little research has however been done on a general framework that encompasses several of these problems. In earlier work we showed how constraint programming (CP) can offer such a general framework; unfortunately, however, we also found that out-of-the-box CP solvers lack the efficiency and scalability achieved by specialized itemset mining systems, which could discourage their use. Here we study the question whether a framework can be built that inherits the generality of CP systems and the efficiency of specialized algorithms. We propose a CP-based framework for pattern mining that avoids the redundant representations and propagations found in existing CP systems. We show experimentally that an implementation of this framework performs comparable to specialized itemset mining systems; furthermore, under certain conditions it lists itemsets with polynomial delay, which demonstrates that it also is a promising approach for analyzing pattern mining tasks from more theoretical perspectives. This is illustrated on a graph mining problem.