Most stream data classification algorithms apply the supervised leaing 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.