XU Wenhua, QIN Zheng. Constructing Decision Trees for Mining High-speed Data Streams[J]. Chinese Journal of Electronics, 2012, 21(2): 215-220.
Citation: XU Wenhua, QIN Zheng. Constructing Decision Trees for Mining High-speed Data Streams[J]. Chinese Journal of Electronics, 2012, 21(2): 215-220.

Constructing Decision Trees for Mining High-speed Data Streams

  • Received Date: 2010-11-01
  • Rev Recd Date: 2011-07-01
  • Publish Date: 2012-04-25
  • Very fast decision tree is one of the most successful and prominent algorithms specifically designed for stream data classification. In this paper, we develop a new decision tree induction model CFDT (Clustering feature decision tree model), which is an extension to VFDT (Very fast decision tree). 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. Moreover, micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce any-time property. Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while also generating high classification accuracy with high speed.
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    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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