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Heidelberg, Germany
Apr. 2, 2001 to Apr. 6, 2001
ISBN: 0-7695-1001-9
pp: 0433
Jian Pei , Simon Fraser University
Jiawei Han , Simon Fraser University
Laks V.S. Lakshmanan , Concordia University & IIT
ABSTRACT
Abstract: Recent work has highlighted the importance of the constraint-based mining paradigm in the context of frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases. In this paper, we study constraints which cannot be handled with existing theory and techniques. For example, avg(S)\theta v, median(S)\theta v, sum(S)\theta v(S can contain items of arbitrary values) (\theta\in\{\geq,\leq\}), are customarily regarded as "tough" constraints in that they cannot be pushed inside an algorithm such as a priori. We develop notion of convertible constraints and systematically analyze, classify, and characterize this class. We also develop techniques which enable them to be readily pushed deep inside the recently developed FP-growth algorithm for frequent itemset mining. Results from our detailed experiments show the effectiveness of the techniques developed.
CITATION
Jian Pei, Jiawei Han, Laks V.S. Lakshmanan, "Mining Frequent Itemsets with Convertible Constraints", ICDE, 2001, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2001, pp. 0433, doi:10.1109/ICDE.2001.914856
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