DocumentCode :
3426554
Title :
Maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams
Author :
Wang, Yongyan ; Li, Kun ; Wang, Hongan
Author_Institution :
Intell. Eng. Lab., Chinese Acad. of Sci., Beijing
fYear :
2009
fDate :
March 30 2009-April 2 2009
Firstpage :
381
Lastpage :
388
Abstract :
Mining frequent itemsets over online data streams, where the new data arrive and the old data will be removed with high speed, is a challenge for the computational complexity. Existing approximate mining algorithms suffer from explosive computational complexity when decreasing the error parameter, isin, which is used to control the mining accuracy. We propose a new approximate mining algorithm using an approximate frequent itemset tree (abbreviated as AFI-tree), called AFI algorithm, to mine approximate frequent itemsets over online data streams. The AFI-tree based on prefix tree maintains only frequent itemsets, so the number of nodes in the tree is very small. All the infrequent child nodes of any frequent node are pruned and the maximal support of the pruned nodes is estimated to detect new frequent itemsets. In order to guarantee the mining accuracy, when the estimated maximal support of the pruned nodes is a bit more than the minimum support, their supports will be re-computed and the frequent nodes among them will be inserted into the AFI-tree. Experimental results show that the AFI algorithm consumes much less memory space than existing algorithms, and runs much faster than existing algorithms in most occasions.
Keywords :
approximation theory; computational complexity; data mining; trees (mathematics); AFI-tree; approximate frequent itemset mining algorithm; computational complexity; data mining; online data stream; prefix tree; Computational complexity; Data mining; Databases; Degradation; Error correction; Explosives; Financial management; Itemsets; Monitoring; Telecommunication network management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2765-9
Type :
conf
DOI :
10.1109/CIDM.2009.4938675
Filename :
4938675
Link To Document :
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