A New Algorithm for Mining Frequent Closed Itemsets from Data Streams

被引：0
|
作者：
Mao, Guojun [1 ]
Yang, Xialing [1 ]
Wu, Xindong [2 ]
机构：
[1] Beijing Univ Technol, Sch Comp Sci, Beijing 100022, Peoples R China
[2] Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA
来源：
2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年
关键词：
data mining; data stream; frequent closed itemset;
D O I：
10.1109/WCICA.2008.4592916
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Mining frequent closed itemsets from data streams has been studied extensively. Algorithm MOMENT and its modified algorithm A-MOMENT were regarded as typical methods. Both of them depend on a data structure named CET. This paper designs a new data structure FULL-CET and proposes a new mining frequent closed itemsets algorithm MFCIDS based on landmark window. Differing entirely from traditional methods which find new frequent itemsets through union operations on existed frequent itemsets, MFCIDS records the support of each closed frequent itemset to maintain all frequent closed itemsets through intersection operations on nodes appearing actually in transactions. Experimental results show that MFCIDS performs better than MOMENT and its modified algorithm A-MOMENT on efficiency and scalability.
引用
收藏
页码：154 / +
页数：3
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