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

被引：1

作者：

Mei, Qingling ^{[1
]}

Chen, Ling ^{[1
]}

机构：

[1] Yangzhou Univ, Dept Comp Sci, Yangzhou 225009, Peoples R China

来源：

MECHANICAL AND ELECTRONICS ENGINEERING III, PTS 1-5 | 2012年 / 130-134卷

关键词：

Stream data mining; frequent data item; fading factor; Hash function; ITEMSETS;

D O I：

10.4028/www.scientific.net/AMM.130-134.2661

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

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.

引用

页码：2661 / 2665

页数：5

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