Frequent Items Mining on Data Stream Using Hash-Table and Heap

被引：3

作者：

Shan, Zhang ^{[1
]}

Ling, Chen ^{[1
]}

Li, Tu ^{[2
]}

机构：

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

[2] Nanjing Univ Aeronaut & Astronaut, Inst Informat Sci & Technol, Nanjing, Jiangsu, Peoples R China

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年

关键词：

data mining; data stream; frequent Items; time fading model; hash table; heap; ALGORITHM;

D O I：

10.1109/ICICISYS.2009.5357918

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Most of the existing algorithms for mining frequent Items on data stream do not emphasis the importance of the recent data items We present an algorithm to detect the items with frequency counts exceeding a user-specified threshold Our algorithm uses a hash table L and a heap to record the potential frequent Items, and can detect c-approximate frequent data items on data stream using O(ILI+ epsilon(1)) memory space and the processing time for each data item is O(log epsilon(1)) Experimental results on several artificial and real datasets show our algorithm has higher precision, requires less memory and consumes less computation time than other similar methods

引用

页码：141 / +

页数：2

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