Mining frequent items based on Bloom Filter

被引：0
|
作者：
Wang, ShuYun [1 ]
Hao, XiuLan [1 ]
Xu, HeXiang [1 ]
Hu, YunFa [1 ]
机构：
[1] Fudan Univ, Dept Comp & Informat Technol, Shanghai, Peoples R China
来源：
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 4, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/FSKD.2007.400
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduce the algorithm MIBFD(Mining frequent Items using Bloom Filter based on Damped model) for mining recent frequent items in data streams. Based on an efficient data structure named extensible and scalable Bloom Filter(ESBF), MIBFD is able to adjust the size of memory used dynamically Theoretical analysis and experiments show that MIBFD is efficient both in processing time and in memory usage.
引用
收藏
页码：679 / 683
页数：5
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