DocumentCode :
2688642
Title :
Survey of the study on frequent pattern mining in data streams
Author :
Wang Jinlong ; Xu Congfu ; Weidong, Chen ; Yunhe, Pan
Author_Institution :
Inst. of Artificial Intelligence, Zhejiang Univ., China
Volume :
6
fYear :
2004
fDate :
10-13 Oct. 2004
Firstpage :
5917
Abstract :
Data mining and knowledge discovery in data streams have recently attracted more attentions for their applications to numerous types of data, including Web clickstreams, sensor networks, etc. Because of some special characteristics, such as continuous arrival in multiple, rapid, time-varying, possibly unpredictable and unbounded, data streams have yielded some fundamentally new research problems. Among the various topics in this research field, it is paramount to find frequent patterns in data streams in a single pass, or a small number of passes, while using less space of memory. This survey reviewed the last advances in the study on frequent pattern mining in data streams, especially classified the present mining algorithms for the first time and discussed them in detail, and finally suggested some promising research directions in the future.
Keywords :
data mining; pattern classification; data mining; data streams; knowledge discovery; pattern mining; Algorithm design and analysis; Artificial intelligence; Data mining; Information analysis; Laboratories; Monitoring; Real time systems; Sensor phenomena and characterization; Sensor systems; Telecommunication traffic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2004 IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-8566-7
Type :
conf
DOI :
10.1109/ICSMC.2004.1401141
Filename :
1401141
Link To Document :
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