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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.8 P.615-628

http://doi.org/10.1631/jzus.C1000330


Clustering feature decision trees for semi-supervised classification from high-speed data streams


Author(s):  Wen-hua Xu, Zheng Qin, Yang Chang

Affiliation(s):  Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China, School of Software, Tsinghua University, Beijing 100084, China

Corresponding email(s):   xwh07@mails.tsinghua.edu.cn, zhqing@mails.tsinghua.edu.cn

Key Words:  Clustering feature vector, Decision tree, Semi-supervised learning, Stream data classification, Very fast decision tree


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Wen-hua Xu, Zheng Qin, Yang Chang. Clustering feature decision trees for semi-supervised classification from high-speed data streams[J]. Journal of Zhejiang University Science C, 2011, 12(8): 615-628.

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%A Yang Chang
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T1 - Clustering feature decision trees for semi-supervised classification from high-speed data streams
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A1 - Yang Chang
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DOI - 10.1631/jzus.C1000330


Abstract: 
Most stream data classification algorithms apply the supervised learning strategy which requires massive labeled data. Such approaches are impractical since labeled data are usually hard to obtain in reality. In this paper, we build a clustering feature decision tree model, CFDT, from data streams having both unlabeled and a small number of labeled examples. CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction. Micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce the any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while generating high classification accuracy with high speed.

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