An Algorithm for Mining Approximate Frequent Itemsets Over Data Streams

摘要：

It is much more difficult to mining frequent itemsets over data streams than traditional data model because data stream has the following characters: unbounded volume of data,rapid arriving rate of records,uncontrollability of records arriving order,etc. A novel algorithm is devised based on Lossy Counting to mine frequent itemsets. Logarithmic tilted time window with an attenuation coefficient is adopted to emphasize the importance of new data. Multilayer count queue mode is designed to not only avoid the counter overflowing but also query top-K itemsets quickly using a index table.

关键词： data stream frequent itemsets logarithmic tilted time window

作者: Na Su Zhehui Wu

作者单位: Department of Information Engineering,ShanDong University of Science and Technology,Taian ShanDong, College of Information Science and Engineering,ShanDong University of Science and Technology,Qingdao

会议类型: 国际会议

会议名称: 2011 International Conference on Opto-Electronics Engineering and Information Science(2011光电电子工程与信息科学国际会议 ICOEIS 2011)

会议地点: 西安

会议语种:英文

页码: 1444-1447

在线出版日期: 2011-12-23（万方平台首次上网日期，不代表论文的发表时间）