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http://dx.doi.org/10.5391/JKIIS.2010.20.1.094

Streaming Decision Tree for Continuity Data with Changed Pattern  

Yoon, Tae-Bok (성균관대학교 컴퓨터공학과)
Sim, Hak-Joon (성균관대학교 컴퓨터공학과)
Lee, Jee-Hyong (성균관대학교 컴퓨터공학과)
Choi, Young-Mee (성결대학교 멀티미디어공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.1, 2010 , pp. 94-100 More about this Journal
Abstract
Data Mining is mainly used for pattern extracting and information discovery from collected data. However previous methods is difficult to reflect changing patterns with time. In this paper, we introduce Streaming Decision Tree(SDT) analyzing data with continuity, large scale, and changed patterns. SDT defines continuity data as blocks and extracts rules using a Decision Tree's learning method. The extracted rules are combined considering time of occurrence, frequency, and contradiction. In experiment, we applied time series data and confirmed resonable result.
Keywords
Streaming Data Mining; Continuity Data Analysis; Streaming Decision Tree;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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