NDLI: An improved multi-support Apriori algorithm under the fuzzy item association condition

Content Provider	IEEE Xplore Digital Library
Author	Hangbin Li Shuhua Chen Jianchen Li Shuo Wang Yihang Fu
Copyright Year	2011
Description	Author affiliation: School of Computer, Wuhan University, Wuhan, China, 430072 (Hangbin Li; Shuhua Chen; Jianchen Li; Shuo Wang; Yihang Fu)
Abstract	Apriori Algorithm has been successfully used in the mining of association rules. The algorithm with multiple-support is proposed for solving “rare item” problem, which means the common items and rare items cannot be satisfied within single support. However, the detailed identification of items will cause the poor mining rules, while the multi-level approach distinguishes the fuzzy association between items in real condition. This paper proposed a novel model based on tags to solve the problem. Each item has various tags, which implies the common attributes. This model is adaptive to the reality since certain item set can be accurately derived from whole sets using combination of tags by users as they like. A corresponding improved multi-support Apriori algorithm is also presented for a recommendation system. The experimental results demonstrated that the new model and the approach are more effective and adaptive.
Starting Page	3539
Ending Page	3542
File Size	362020
Page Count	4
File Format	PDF
ISBN	9781457703201
e-ISBN	9781457703218
DOI	10.1109/ICECC.2011.6067661
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Adaptation models Personalized service Estimation Aprioiri algorithm Multiple support Classification algorithms Transaction databases Tag Association rules
Content Type	Text
Resource Type	Article

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