NDLI: Probability-based incremental association rule discovery using the normal approximation

Content Provider	IEEE Xplore Digital Library
Author	Ariya, A. Kreesuradej, W.
Copyright Year	2013
Description	Author affiliation: Fac. of Inf. Technol., King Mongkut's Inst. of Technol. Ladkrabang, Bangkok, Thailand (Ariya, A.; Kreesuradej, W.)
Abstract	An incremental association rules mining is one of an association rule mining research topics which finds the relation between set of item in dynamic databases. As data grows up rapidly, the co-occurrence itemset which discovered in the previous mining may be changed and the association rule will be change consequently. Incremental association rule mining research attempts to maintain that rules. Probability-based algorithm, one of an incremental algorithm, applied the principle of Bernoulli trial to predict expected frequent itemsets for reducing collected border itemsets and a number of times to rescan the original database. However, the numerical problem will occur when the algorithm deals with a large database. To manipulate with this problem, the improved probability-based incremental association rule discovery using normal approximation to estimate the probability of occurrence of expected frequent itemset is introduced in this paper. In addition, the confidence interval is applied to ensure that the collecting of expected frequent itemsets is properly kept.
Starting Page	432
Ending Page	439
File Size	134679
Page Count	8
File Format	PDF
ISBN	9781479910502
DOI	10.1109/IRI.2013.6642503
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Itemsets Association rules Approximation methods Prediction algorithms Approximation algorithms Probability Normal Approximation Data Mining Incremental Association Rule Discovery
Content Type	Text
Resource Type	Article

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