An Algorithm for Mining Frequent Stream Data Items Using Hash Function and Fading Factor

摘要：

A new algorithm to mine the frequent items in data stream is presented. The algorithm adopts a time fading factor to emphasize the importance of the relatively newer data, and records the densities of the data items in Hash tables. For a given threshold of density S and an integer k, our algorithm can mine the top k frequent items. Computation time for processing each data item is O(1). Experimental results show that the algorithm outperforms other methods in terms of accuracy, memory requirement, and processing speed.

关键词： Stream data mining frequent data item fading factor Hash function

作者: Qingling Mei Ling Chen

作者单位: Department of Computer Science, Yangzhou University, Yangzhou China, 225009 Department of Computer Science, Yangzhou University, Yangzhou China, 225009 State Key Lab of Novel S

会议类型: 国际会议

会议名称: 2011 3nd International Conference on Mechanical and Electronics Engineering(2011年第三届机械与电子工程国际会议 ICMEE2011)

会议地点: 合肥

会议语种:英文

页码: 2661-2665

在线出版日期: 2011-09-23（万方平台首次上网日期，不代表论文的发表时间）