NDLI: Dynamic miss-counting algorithms: finding implication and similarity rules with confidence pruning

Content Provider	IEEE Xplore Digital Library
Author	Fujiwara, S. Ullman, J.D. Motwani, R.
Copyright Year	2000
Description	Author affiliation: Dept. of Comput. Sci., Stanford Univ., CA, USA (Fujiwara, S.)
Abstract	Dynamic miss-counting (DMC) algorithms are proposed which find all implication and similarity rules with confidence pruning but without support pruning. To handle data sets with a large number of columns, we propose dynamic pruning techniques that can be applied during data scanning. DMC counts the numbers of rows in which each pair of columns disagree instead of counting the number of hits. DMC deletes a candidate as soon as the number of misses exceeds the maximum number of misses allowed for that pair. We also propose several optimization techniques that reduce the required memory size significantly. We evaluated our algorithms by using four data sets, viz. Web access logs, a Web page-link graph, news documents and a dictionary. These data sets have between 74,000 and 700,000 items. Experiments show that DMC can find high-confidence rules for such large data sets efficiently.
Starting Page	501
Ending Page	511
File Size	146583
Page Count	11
File Format	PDF
ISBN	0769505066
ISSN	10636382
DOI	10.1109/ICDE.2000.839449
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-03-03
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heuristic algorithms Data mining Computer science Frequency Dictionaries Collaboration Filtering Association rules
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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