NDLI: An Efficient Association Rule Mining Algorithm and Business Application

Content Provider	IEEE Xplore Digital Library
Author	Zhang Zheng Wang Hui-wen
Copyright Year	2007
Description	Author affiliation: Beijing Univ., Beijing (Zhang Zheng; Wang Hui-wen)
Abstract	In this paper, aim at the inefficient problem of the a priori algorithms, we design a new matrix data structure, called cooccurrence matrix, in short COM, to store the data information instead of directly using the transactional database. In COM, any item sets can be randomly accessed and counted without many times full scan of the original transactional database. Based on COM, we first divide association rule into two kinds of rule and then we present an efficient algorithms (COM_mining) to find the valid association rules among the frequent items. Finally we apply COM_mining algorithm and a priori algorithm simultaneously to analyze up-down association relationship between various industry stock blocks of China A stock market. From analytical result we can find that in China A stock market, there are indeed up-down association relationship between various industry stock blocks. At the same time, through comparing COM_mining algorithm and a priori algorithm in this application, we can see, COM_mining is more efficient than a priori.
Starting Page	959
Ending Page	965
File Size	4341148
Page Count	7
File Format	PDF
ISBN	9781424414734
DOI	10.1109/ICCCAS.2007.4348207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Data analysis Industrial relations Itemsets Data structures Frequency Transaction databases Association rules Data mining Stock markets
Content Type	Text
Resource Type	Article

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