挖掘数据流任意滑动时间窗口内频繁模式-CiteSeerX.PDF

ISSN 1000-9825, CODEN RUXUEW E-mail: jos@ Journal of Software , Vol.19, No.10, October 2008, pp.2585−2596 DOI: 10.3724/SP.J.1001.2008.02585 Tel/Fax: +86-10 © 2008 by Journal of Software . All rights reserved. ∗ 挖掘数据流任意滑动时间窗口内频繁模式 李国徽, 陈 辉+ (华中科技大学 计算机科学与技术学院,湖北 武汉 430074) Mining the Frequent Patterns in an Arbitrary Sliding Window over Online Data Streams LI Guo-Hui, CHEN Hui+ (School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China) + Corresponding author: E-mail: chen_hui@ Li GH, Chen H. Mining the frequent patterns in an arbitrary sliding window over online data streams. Journal of Software, 2008,19(10):2585−2596. /1000-9825/19/2585.htm Abstract : Because of the fluidity and continuity of data stream, the knowledge embedded in stream data is most likely to be changed as time goes by. Thus, in most data stream applications, people are more interested in the information of the recent transactions than that of the old. This paper proposes a method for mining the frequent patterns in an arbitrary sliding window of data streams. As data stream flows, the contents of the data stream are captured with a compact prefix-tree by scanning the stream only once. And the obsolete and infrequent items are deleted by periodically pruning the tree. To differentiate the patterns of recently generated transactions from those of historic transactions, a time decaying model is also applied. Extensive simulations are conducted and the experimental results show that the proposed method is efficient and scalable, and also superior to other analogous algorithms. Key words: