NDLI: Extracting Non-redundant Approximate Rules from Multi-level Datasets

Content Provider	IEEE Xplore Digital Library
Author	Shaw, G. Yue Xu Geva, S.
Copyright Year	2008
Description	Author affiliation: Fac. of Inf. Technol., Queensland Univ. of Technol., Brisbane, QLD (Shaw, G.; Yue Xu; Geva, S.)
Abstract	Association rule mining plays an important job in knowledge and information discovery. Often the number of the discovered rules is huge and many of them are redundant, especially for multi-level datasets. Previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem, with work in focusing on single level datasets. Recent work by Shaw et. al. has extended the non-redundant approaches presented in to include the elimination of redundant exact basis rules from multi-level datasets. In this paper, we propose an extension to the work in to allow for the removal of hierarchically redundant approximate basis rules from multi-level datasets through the use of the datasetpsilas hierarchy or taxonomy. Experimentation shows our approach can effectively generate both multi-level and cross level non-redundant rule sets which are lossless.
Starting Page	333
Ending Page	340
File Size	387433
Page Count	8
File Format	PDF
ISBN	9780769534404
ISSN	10823409
DOI	10.1109/ICTAI.2008.54
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association rules Data mining Itemsets Taxonomy Artificial intelligence Information technology Australia Data analysis Transaction databases Data structures multi-level datasets non-redundant association rule mining
Content Type	Text
Resource Type	Article

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