Mining Association Rules in Multidimensional Stream Data 


Vol. 13,  No. 6, pp. 765-774, Oct.  2006
http://dx.doi.org/10.3745/KIPSTD.2006.13D.6.765


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  Abstract

An association rule discovery, a technique to analyze the stored data in databases to discover potential information, has been a popular topic in stream data system. Most of the previous researches are concerned to single stream data. However, this approach may ignore in mining to multidimensional stream data. In this paper, we study the techniques discovering the association rules to multidimensional stream data. And we propose a AR-MS method reflecting the characteristics of stream data since make the summarization information by one data scan and discovering the association rules for significant rare data that appear infrequently in the database but are highly associated with specific event. Also, AR-MS method can discover the maximal frequent item of multidimensional stream data by using the summarization information. Through analysis and experiments, we show that AR-MS method is superior to other previous methods.

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[IEEE Style]

K. Dae-In, P. Joon, K. Hong-Ki, H. Bu-Hyun, "Mining Association Rules in Multidimensional Stream Data," The KIPS Transactions:PartD, vol. 13, no. 6, pp. 765-774, 2006. DOI: http://dx.doi.org/10.3745/KIPSTD.2006.13D.6.765.

[ACM Style]

Kim Dae-In, Park Joon, Kim Hong-Ki, and Hwang Bu-Hyun. 2006. Mining Association Rules in Multidimensional Stream Data. The KIPS Transactions:PartD, 13, 6, (2006), 765-774. DOI: http://dx.doi.org/10.3745/KIPSTD.2006.13D.6.765.

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