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PAKDD
2007
ACM

Mining Frequent Itemsets from Uncertain Data

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Mining Frequent Itemsets from Uncertain Data
Abstract. We study the problem of mining frequent itemsets from uncertain data under a probabilistic framework. We consider transactions whose items are associated with existential probabilities and give a formal definition of frequent patterns under such an uncertain data model. We show that traditional algorithms for mining frequent itemsets are either inapplicable or computationally inefficient under such a model. A data trimming framework is proposed to improve mining efficiency. Through extensive experiments, we show that the data trimming technique can achieve significant savings in both CPU cost and I/O cost.
Chun Kit Chui, Ben Kao, Edward Hung
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where PAKDD
Authors Chun Kit Chui, Ben Kao, Edward Hung
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