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Erschienen in:
2008 | OriginalPaper | Buchkapitel
A Decremental Approach for Mining Frequent Itemsets from Uncertain Data
verfasst von : Chun-Kit Chui, Ben Kao
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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We study the problem of mining frequent itemsets from
uncertain data
under a
probabilistic model
. We consider transactions whose items are associated with
existential probabilities
. A
decremental pruning
(DP) technique, which exploits the statistical properties of items’ existential probabilities, is proposed. Experimental results show that DP can achieve significant computational cost savings compared with existing approaches, such as U-Apriori and LGS-Trimming. Also, unlike LGS-Trimming, DP does not require a user-specified trimming threshold and its performance is relatively insensitive to the population of low-probability items in the dataset.