Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 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
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Optimization of association rule mining queries
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Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Soft constraint based pattern mining
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Data & Knowledge Engineering
Constraint programming for itemset mining
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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
Data & Knowledge Engineering
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Itemset mining: A constraint programming perspective
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A new class of constraints for constrained frequent pattern mining
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A constrained frequent pattern mining system for handling aggregate constraints
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Data Mining and Knowledge Discovery
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In the last years, in the context of the constraint-based pattern discovery paradigm, properties of constraints have been studied comprehensively and on the basis of this properties, efficient constraint-pushing techniques have been defined. In this paper we review and extend the state-of-the-art of the constraints that can be pushed in a frequent pattern computation. We introduce novel data reduction techniques which are able to exploit convertible anti-monotone constraints (e.g., constraints on average or median) as well as tougher constraints (e.g., constraints on variance or standard deviation). A thorough experimental study is performed and it confirms that our framework outperforms previous algorithms for convertible constraints, and exploit the tougher ones with the same effectiveness. Finally, we highlight that the main advantage of our approach, i.e., pushing constraints by means of data reduction in a level-wise framework, is that different properties of different constraints can be exploited all together, and the total benefit is always greater than the sum of the individual benefits. This consideration leads to the definition of a general Apriori-like algorithm which is able to exploit all possible kinds of constraints studied so far.