Volume 4 Number 3 (Mar. 2009)
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JCP 2009 Vol.4(3): 208-214 ISSN: 1796-203X
doi: 10.4304/jcp.4.3.208-214

Stream Data Classification Using Improved Fisher Discriminate Analysis

Chen Ling1, 2, Zou Ling-Jun1, Tu Li3
1Department of Computer Science, Yangzhou University, Yangzhou, China
2State Key Lab of Novel Software Tech, Nanjing University, Nanjing, China
3College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China


Abstract—A modified Fisher discriminate analysis method for classifying stream data is presented. To satisfy the realtime demand in classifying stream data, this method defines a new criterion for Fisher discriminate analysis. Since the new criterion requires less computation and memory space, it is much faster and more suitable for online processing in stream data environment. It can overcome the problem of singular within-class scatter matrix in traditional FDA. Our algorithm speeds up the mining process while maintaining the high classification accuracy and capturing the up-todate trends in the stream. Experiments on real and synthetic data sets show that our algorithm can improve the classification accuracy and speed for stream data classification.

Index Terms—Data mining, classification, Fisher discriminate analysis.

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Cite: Chen Ling, Zou Ling-Jun, Tu Li, "Stream Data Classification Using Improved Fisher Discriminate Analysis," Journal of Computers vol. 4, no. 3, pp. 208-214, 2009.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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