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http://dx.doi.org/10.3745/KIPSTD.2012.19D.2.139

A Method for Frequent Itemsets Mining from Data Stream  

Seo, Bok-Il (전남대학교 전자컴퓨터공학부)
Kim, Jae-In (전남대학교 전자컴퓨터공학부)
Hwang, Bu-Hyun (전남대학교 전자컴퓨터공학부)
Publication Information
The KIPS Transactions:PartD / v.19D, no.2, 2012 , pp. 139-146 More about this Journal
Abstract
Data Mining is widely used to discover knowledge in many fields. Although there are many methods to discover association rule, most of them are based on frequency-based approaches. Therefore it is not appropriate for stream environment. Because the stream environment has a property that event data are generated continuously. it is expensive to store all data. In this paper, we propose a new method to discover association rules based on stream environment. Our new method is using a variable window for extracting data items. Variable windows have variable size according to the gap of same target event. Our method extracts data using COBJ(Count object) calculation method. FPMDSTN(Frequent pattern Mining over Data Stream using Terminal Node) discovers association rules from the extracted data items. Through experiment, our method is more efficient to apply stream environment than conventional methods.
Keywords
Data Mining; Data Stream; Frequent Itemsets; Real-time Mining;
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