An Efficient Algorithm for Finding Frequent Items in a Stream

被引：2
|
作者：
Tu, Li [1 ]
Chen, Ling [2 ,3 ]
Zhang, Shan [2 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Jiangyin Polytech Coll, Inst Informat Sci & Technol, Dept Comp Sci, Nanjing 210016, Jiangyin, Peoples R China
[2] Yangzhou Univ, Dep Comp Sci, Yangzhou, Jiangsu, Peoples R China
[3] Nanjing Univ, Natl Key Lab Novel Software Tech, Nanjing, Peoples R China
来源：
PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON ELECTRONIC COMMERCE AND SECURITY, VOL II | 2009年
关键词：
data stream; data mining; frequent items; fading factor;
D O I：
10.1109/ISECS.2009.188
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Most of the existing algorithms for mining frequent items over data streams do not emphasis the importance of the more recent data items. We present an efficient algorithm where a fading factor lambda is used for computing frequency counts exceeding a user-specified threshold over data streams. Our algorithm lambda-Miner can detect epsilon-approximate frequent items of a data stream using O(epsilon(-1)) memory space and the processing time for each data item is O(1). Experimental results on several artificial data sets and real data sets show that lambda-Miner performs better than lambda-LC in terms with precision, memory requirement and time cost.
引用
收藏
页码：200 / +
页数：2
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