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Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight... Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees MATTEO RIONDATO and ELI UPFAL, Brown University The tasks of extracting (top-K) Frequent Itemsets (FIs) and Association Rules (ARs) are fundamental primitives in data mining and database applications. Exact algorithms for these problems exist and are widely used, but their running time is hindered by the need of scanning the entire dataset, possibly multiple times. High-quality approximations of FIs and ARs are sufficient for most practical uses. Sampling techniques can be used for fast discovery of approximate solutions, but works exploring this technique did not provide satisfactory performance guarantees on the quality of the approximation due to the difficulty of bounding the probability of under- or oversampling any one of an unknown number of frequent itemsets. We circumvent this issue by applying the statistical concept of Vapnik-Chervonenkis (VC) dimension to develop a novel technique for providing tight bounds on the sample size that guarantees approximation of the (top-K) FIs and ARs within user-specified parameters. The resulting sample size is linearly dependent on the VC-dimension of a range space associated with the dataset. We analyze the VC-dimension of this range space and show that it http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/2629586
Publisher site
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Abstract

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees MATTEO RIONDATO and ELI UPFAL, Brown University The tasks of extracting (top-K) Frequent Itemsets (FIs) and Association Rules (ARs) are fundamental primitives in data mining and database applications. Exact algorithms for these problems exist and are widely used, but their running time is hindered by the need of scanning the entire dataset, possibly multiple times. High-quality approximations of FIs and ARs are sufficient for most practical uses. Sampling techniques can be used for fast discovery of approximate solutions, but works exploring this technique did not provide satisfactory performance guarantees on the quality of the approximation due to the difficulty of bounding the probability of under- or oversampling any one of an unknown number of frequent itemsets. We circumvent this issue by applying the statistical concept of Vapnik-Chervonenkis (VC) dimension to develop a novel technique for providing tight bounds on the sample size that guarantees approximation of the (top-K) FIs and ARs within user-specified parameters. The resulting sample size is linearly dependent on the VC-dimension of a range space associated with the dataset. We analyze the VC-dimension of this range space and show that it

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery

Published: Aug 1, 2014

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