Mining Association Rule for the Abnormal Event in Data Stream Systems


The KIPS Transactions:PartD, Vol. 14, No. 5, pp. 483-490, Aug. 2007
10.3745/KIPSTD.2007.14.5.483,   PDF Download:

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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
D. I. Kim, J. Park, B. H. Hwang, "Mining Association Rule for the Abnormal Event in Data Stream Systems," The KIPS Transactions:PartD, vol. 14, no. 5, pp. 483-490, 2007. DOI: 10.3745/KIPSTD.2007.14.5.483.

[ACM Style]
Dae In Kim, Joon Park, and Bu Hyun Hwang. 2007. Mining Association Rule for the Abnormal Event in Data Stream Systems. The KIPS Transactions:PartD, 14, 5, (2007), 483-490. DOI: 10.3745/KIPSTD.2007.14.5.483.