JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 4    Issue : 3    Date : March 2009

Stream Data Classification Using Improved Fisher Discriminate Analysis
Ling Chen, Ling-Jun Zou, Li Tu
Page(s): 208-214
Full Text:
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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