| Title: | maintaining only frequent itemsets to mine approximate frequent itemsets over online data streams |
| Author: | Wang Yongyan
; Li Kun
; Wang Hongan
|
| Source: | 2009 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2009 - Proceedings
|
| Conference Name: | IEEE Symposium on Computational Intelligence and Data Mining
|
| Conference Date: | MAR 30-APR
|
| Issued Date: | 2009
|
| Conference Place: | Nashville, TN
|
| Keyword: | Algorithms
; Artificial intelligence
; Computational complexity
; Data communication systems
; Data mining
|
| Publisher: | 2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING
|
| Publish Place: | 345 E 47TH ST, NEW YORK, NY 10017 USA
|
| ISBN: | 978-1-4244-2765-9
|
| Department: | Wang, Yongyan; Li, Kun; Wang, Hongan Chinese Acad Sci, Inst Software, Intelligence Engn Lab, Beijing, Peoples R China.
|
| Sponsorship: | IEEE
|
| English 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, 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. |
| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/8318
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| 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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| maintaining only frequent itemsets to mine approximate frequent itemsets over.pdf（457KB） | -- | -- | 限制开放 | -- | 联系获取全文 |
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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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