Classification Algorithm Combined with Unsupervised Learning for Data Stream
XU Shuliang1 , WANG Junhong1,2
1.School of Computer and Information Technology, Shanxi University, Taiyuan 030006 2.Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
Abstract An ensemble learning techniques based algorithm combined with unsupervised learning is proposed for concept drift problem of data stream. An attribute reduction mechanism is introduced into classification process and then a clustering algorithm is applied to the data for clustering. Accuracies of classification and clustering are compared to decide whether concept drift appears or not. The experimental results show that the proposed algorithm efficiently decreases time consumption and improves the precision.
About author:: XU Shuliang, born in 1989, master student. His research interests include data streams classification, concept drift detection and extreme learning machine.WANG Junhong(Corresponding author), born in 1979, Ph.D., associate professor. Her research interests include data mining and machine learning.
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2016, Vol. 29 
