Finding Association Rules based on the Significant Rare Relation of Events with Time Attribute 


Vol. 16,  No. 5, pp. 691-700, Oct.  2009
10.3745/KIPSTD.2009.16.5.691


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  Abstract

An event means a flow which has a time attribute such as the a symptom of patients, an interval event has the time period between the start-time-point and the end-time-point. Although there are many studies for temporal data mining, they do not deal with discovering knowledge from interval event such as patient histories and purchase histories. In this paper, we suggest a method of temporal data mining that finds association rules of event causal relationships and predicts an occurrence of effect event based on discovered rules. Our method can predict the occurrence of an event by summarizing an interval event using the time attribute of an event and finding the causal relationship of event. As a result of simulation, this method can discover better knowledge than others by considering a lot of supports of an event and finding the significant rare relation on interval events which means an essential cause of an event, regardless of an occurrence support of an event in comparison with conventional data mining techniques.

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[IEEE Style]

D. Y. Han, D. I. Kim, J. I. Kim, M. J. Song, B. H. Hwang, "Finding Association Rules based on the Significant Rare Relation of Events with Time Attribute," The KIPS Transactions:PartD, vol. 16, no. 5, pp. 691-700, 2009. DOI: 10.3745/KIPSTD.2009.16.5.691.

[ACM Style]

Dae Young Han, Dae In Kim, Jae In Kim, Myung Jin Song, and Bu Hyun Hwang. 2009. Finding Association Rules based on the Significant Rare Relation of Events with Time Attribute. The KIPS Transactions:PartD, 16, 5, (2009), 691-700. DOI: 10.3745/KIPSTD.2009.16.5.691.

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