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http://dx.doi.org/10.3745/KIPSTD.2007.14-D.5.483

Mining Association Rule for the Abnormal Event in Data Stream Systems  

Kim, Dae-In (전남대학교 전자컴퓨터정보통신공학부)
Park, Joon (전남대학교 전산학과)
Hwang, Bu-Hyun (전남대학교 전자컴퓨터정보통신공학부)
Publication Information
The KIPS Transactions:PartD / v.14D, no.5, 2007 , pp. 483-490 More about this Journal
Abstract
Recently mining techniques that analyze the data stream to discover potential information, have been widely studied. However, most of the researches based on the support are concerned with the frequent event, but ignore the infrequent event even if it is crucial. In this paper, we propose SM-AF method discovering association rules to an abnormal event. In considering the window that an abnormal event is sensed, SM-AF method can discover the association rules to the critical event, even if it is occurred infrequently. Also, SM-AF method can discover the significant rare itemsets associated with abnormal event and periodic event itemsets. Through analysis and experiments, we show that SM-AF method is superior to the previous methods of mining association rules.
Keywords
Data Stream; Association Rule; Abnormal Event; Significant Rare Itemsets; Support;
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Times Cited By KSCI : 1  (Citation Analysis)
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