DocumentCode :
1618715
Title :
A New Evolving Data Streams System with Data Fusion
Author :
Huijun, Yu ; Zhigang, Wang ; Xiaoyan, Liu
Author_Institution :
Sch. of Electron. & Inf. Eng., Hunan Univ. of Technol., Zhuzhou, China
fYear :
2012
Firstpage :
1743
Lastpage :
1746
Abstract :
Cluster analysis is an important data mining issue, where objects under investigation are grouped into subsets of the original set of objects. In recent several years, a few clustering algorithms have been developed for the data stream problem. However these algorithms lack of extensibility or efficiency. In this paper we propose a new evolving data streams system with data fusion. We discuss a fundamentally different philosophy for data stream clustering which is guided by application centered requirements. The system is highly suitable for real-time implementation and is demonstrated through a series of experiments. The experiments over a number of real and synthetic data sets illustrate the effectiveness and efficiency.
Keywords :
data mining; pattern clustering; sensor fusion; cluster analysis; clustering algorithms; data fusion; data mining; data streams system; synthetic data; Algorithm design and analysis; Clustering algorithms; Data mining; Real-time systems; Robustness; Streaming media; Time series analysis; Cluster; Data stream; evolving algorithm; fusion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4673-1450-3
Type :
conf
DOI :
10.1109/ICICEE.2012.461
Filename :
6322751
Link To Document :
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