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题名:
maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams
作者: Wang Yongyan ; Li Kun ; Wang Hongan
会议文集: 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
会议名称: IEEE Symposium on Computational Intelligence and Data Mining
会议日期: MAR 30-APR
出版日期: 2009
会议地点: Nashville, TN
关键词: Algorithms ; Artificial intelligence ; Computational complexity ; Data communication systems ; Data mining
出版者: 2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING
出版地: 345 E 47TH ST, NEW YORK, NY 10017 USA
ISBN: 978-1-4244-2765-9
部门归属: Wang, Yongyan; Li, Kun; Wang, Hongan Chinese Acad Sci, Inst Software, Intelligence Engn Lab, Beijing, Peoples R China.
主办者: IEEE
英文摘要: 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, c, 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.
内容类型: 会议论文
URI标识: http://ir.iscas.ac.cn/handle/311060/8318
Appears in Collections:人机交互技术与智能信息处理实验室_会议论文

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Recommended Citation:
Wang Yongyan,Li Kun,Wang Hongan. maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams[C]. 见:IEEE Symposium on Computational Intelligence and Data Mining. Nashville, TN. MAR 30-APR.
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