A More Accurate Space Saving Algorithm for Finding the Frequent Items

被引：0

作者：

Zhou Jun ^{[1
]}

Chen Ming ^{[1
]}

Xiong Huan ^{[2
]}

机构：

[1] PLAUST, Inst Command Automat, Dept Comp Sci, Nanjing, Peoples R China

[2] China Elect Syst Engn Res Inst, Dept Comp Network, Beijing, Peoples R China

来源：

2010 2ND INTERNATIONAL WORKSHOP ON DATABASE TECHNOLOGY AND APPLICATIONS PROCEEDINGS (DBTA) | 2010年

关键词：

component; data stream; frequent items; LRU; NetFlow; anomaly detection;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The frequent items problem is to process a stream as a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Aiming at higher false positive rate of the Space-Saving algorithm, an LRU-based (Least Recently Used, LRU) improved algorithm with low frequency item pre-eliminated is proposed. Accuracy, stability and adaptability of the improved algorithm have been apparently enhanced. Experimental results indicate that the algorithm can not only be used to find the frequent items, and can be used to estimate the frequency of them precisely. The improved algorithm can be used for online processing both high-speed network packet stream and backbone NetFlow stream.

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页数：5

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