NDLI: A prefix tree-based model for mining association rules from quantitative temporal data

Content Provider	IEEE Xplore Digital Library
Author	Yo-Ping Huang Li-Jen Kao Sandnes, F.-E.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Tatung Univ., Taipei, Taiwan (Yo-Ping Huang; Li-Jen Kao)
Abstract	There are two problems as we use conventional Boolean association rules mining algorithm to discover temporal association rules over the stock market to predict stock price variation. The first problem is that the discovered rules only consider associations between the presence and absence of variations of stock prices and the second problem is that the associations among stock price variations are within the same transaction day. For example, if stock A raises, then stock B raises the same day. This Boolean temporal association rule reveals no information of quantitative variations of stock prices and can only predict price trend in the same day. In this paper, we deal with the problem of mining temporal association rules in stock databases containing quantitative price variations to discover the associations among different transactions day. Our algorithm first employs data discretization concept to partition quantitative attributes into intervals and an adaptive a priori method that cooperates with time sliding window concept and prefix tree is developed to find quantitative temporal association rules. An example of such a rule might be "if stock A price variation raised 5% to 7% and stock B raised 2.5% to 5% the same day, then stock C will raise 0% to 2.5% in the next two days." In this case, the stock price variation is taking into consideration and the associated stock price variations belong to different transaction days. As compared with conventional methods, more useful results can be found from the proposed quantitative temporal association rules.
Starting Page	158
Ending Page	163
File Size	373666
Page Count	6
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571138
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Association rules Electronic mail Companies Computer science Data engineering Educational institutions Stock markets Transaction databases Partitioning algorithms prefix tree data mining quantitative temporal association rules
Content Type	Text
Resource Type	Article

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