• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.483-490
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• 2007

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.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.765-774
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• 2006

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.