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Mining association rules: anti-skew algorithms

Content Provider	IEEE Xplore Digital Library
Author	Jun-Lin Lin Dunham, M.H.
Copyright Year	1998
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Southern Methodist Univ., Dallas, TX, USA (Jun-Lin Lin)
Abstract	Mining association rules among items in a large database has been recognized as one of the most important data mining problems. All proposed approaches for this problem require scanning the entire database at least or almost twice in the worst case. We propose several techniques which overcome the problem of data skew in the basket data. These techniques reduce the maximum number of scans to less than 2, and in most cases find all association rules in about 1 scan. Our algorithms employ prior knowledge collected during the mining process and/or via sampling, to further reduce the number of candidate itemsets and identify false candidate itemsets at an earlier stage.
Starting Page	486
Ending Page	493
File Size	224341
Page Count	8
File Format	PDF
ISBN	0818682892
ISSN	10636382
DOI	10.1109/ICDE.1998.655811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-02-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Association rules Sampling methods Partitioning algorithms Itemsets Computer science Data engineering Transaction databases Decision making Data systems
Content Type	Text
Resource Type	Article

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