TPT: Fast Data Updating Method for Sliding Window Based Frequent Pattern Mining Using Tail Pointer Table

摘要：

Sliding window method is one important way of mining frequent itemsets from data streams. The speed of the sliding window is affected not only by the efficiency of the mining algorithm, but also by the speed of data updating of the window. To increase the data updating speed, proposes a new structure with Tail Pointer Table (TPT) and the corresponding mining algorithm; theoretical analysis and experiments are carried out to prove its effectiveness.

关键词： data stream frequent itemsets sliding window tail pointer table

作者: Le Wang Shui Wang Shilei Shan Lin Feng

作者单位: School of Innovation Experiment, Dalian University of Technology, Dalian, China School of Software, Nanyang Institute of Technology, Nanyang, China

会议类型: 国际会议

会议名称: 2011 International Conference on Database and Data Mining(ICDDM 2011)(2011年数据库和数据挖掘国际会议)

会议地点: 三亚

会议语种:英文

页码: 105-108

在线出版日期: 2011-03-25（万方平台首次上网日期，不代表论文的发表时间）