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12

PAKDD
2007
ACM

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Mining Frequent Itemsets from Uncertain Data

13 years 10 months ago

Mining Frequent Itemsets from Uncertain Data

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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

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Data Mining | Mining Frequent Itemsets | PAKDD 2007 | Uncertain Data | Uncertain Data Model |

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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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