A New Approach for Mining Frequent Items in Data Stream

被引：0
|
作者：
Tu, Li [1 ,2 ]
Chen, Ling [1 ,3 ]
机构：
[1] Yangzhou Univ, Dept Comp Sci, Yangzhou, Jiangsu, Peoples R China
[2] Jiangyin Polytech Coll, Jiangyin, Peoples R China
[3] Nanjing Univ, Natl Key Lab Novel Software Tech, Nanjing, Jiangsu, Peoples R China
来源：
2010 INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT (CCCM2010), VOL II | 2010年
关键词：
data mining; data stream; frequent items; sliding window; ALGORITHM;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
An algorithm named F-Stream for mining frequent items in a stream based on sliding window is proposed. Our algorithm can detect 6-approximate frequent items in a data stream using O(epsilon(-1)) memory space and the processing time for each data item is O(epsilon(-1)). Extensive experimental results show that F-Stream outperforms other methods in terms of accuracy, memory requirement, and processing speed.
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页码：225 / 228
页数：4
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