NDLI: An FP-split method for fast association rules mining

Content Provider	IEEE Xplore Digital Library
Author	Chin-Feng Lee Tsung-Hsien Shen
Copyright Year	2005
Description	Author affiliation: Dept. of Inf. Manage., Chaoyang Univ. of Technol., Taichung, Taiwan (Chin-Feng Lee; Tsung-Hsien Shen)
Abstract	Recently, most of the studies on association rules mining focused on improving the efficiency of frequent itemsets generation. To our best knowledge, the FP-growth algorithm, which is based on the FP-tree to generate frequent itemsets is time-efficient. Currently, relevant studies are introduced to improve the FP-growth algorithm. However, they ignore the fact that the FP-tree construction may spend much time. Therefore, the goal of our research is to propose a fast algorithm called frequent pattern split, simply FP-split, for improving the process of the FP-tree construction. The proposed FP-split algorithm contains two main steps. The first step is to scan a transaction database only once for generating equivalence classes of frequent items. The second step is to sort these equivalence classes of frequent items in descending order so as to construct the FP-split tree. Through detailed experimental evaluations under various system conditions, our method shows excellent performance in terms of execution efficiency and scalability.
Starting Page	459
Ending Page	463
File Size	963820
Page Count	5
File Format	PDF
ISBN	0780389328
DOI	10.1109/ITRE.2005.1503165
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-27
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Association rules Data mining Itemsets Transaction databases Information management Chaos Electronic mail Scalability Information technology Information analysis
Content Type	Text
Resource Type	Article

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