A sliding window algorithm for mining frequent itemsets on data stream

被引：0
|
作者：
Liu, Junqiang [1 ]
Li, Xiurong [1 ]
机构：
[1] Zhejiang Gongshang Univ, Inst Artif Intelligence, Hangzhou, Zhejiang, Peoples R China
来源：
DCABES 2006 PROCEEDINGS, VOLS 1 AND 2 | 2006年
关键词：
data mining; data stream; frequent itemsets; sliding window;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Sonic applications generate continuous, unbounded, and extremely fast data stream. Due to its characteristics, it is difficult to apply the existing productions of mining frequent itemsets in traditional databases to the data stream environment directly. A one-scan algorithm SFP of mining frequent itemsets on data stream was proposed by using a fixed-size sliding window model and adopting a data structure of SPFT. The window limited memory usage. SPFT made use of the advantages of FP-Growth and additionally introduced a pivotal timeldList into the SPFT. TimeldList marked the itemsets contained in those expired transactions so as to remove the effect of old transactions on the mining results as the window slides forward.
引用
收藏
页码：637 / 639
页数：3
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