DocumentCode :
3493262
Title :
A supervised approach for change detection in data streams
Author :
Bondu, A. ; Boullé, M.
Author_Institution :
R&D, EDF, Clamart, France
fYear :
2011
fDate :
July 31 2011-Aug. 5 2011
Firstpage :
519
Lastpage :
526
Abstract :
In recent years, the amount of data to process has increased in many application areas such as network monitoring, web click and sensor data analysis. Data stream mining answers to the challenge of massive data processing, this paradigm allows for treating pieces of data on the fly and overcomes exhaustive data storage. The detection of changes in a data stream distribution is an important issue which application area is wide. In this article, change detection problem is turned into a supervised learning task. We chose to exploit the supervised discretization method “MODL” given its interesting properties. Our approach is favorably compared with an alternative method on artificial data streams, and is applied on real data streams.
Keywords :
data analysis; data mining; learning (artificial intelligence); change detection; data processing; data storage; data stream mining; data streams; network monitoring; sensor data analysis; supervised approach; supervised learning; web click; Current distribution; Data models; Encoding; Monitoring; Nickel; Smoothing methods; Supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
ISSN :
2161-4393
Print_ISBN :
978-1-4244-9635-8
Type :
conf
DOI :
10.1109/IJCNN.2011.6033265
Filename :
6033265
Link To Document :
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