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

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.