When to update the sequential patterns of stream data?

Authors:
Qingguo Zheng;Ke Xu;Shilong Ma
Affiliations:
National Lab of Software Development Environment, Department of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing;National Lab of Software Development Environment, Department of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing;National Lab of Software Development Environment, Department of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

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Abstract

In this paper, we first define a difference measure between the old and new sequential patterns of stream data, which is proved to be a distance. Then we propose an experimental method, called TPD (Tradeoff between Performance and Difference), to decide when to update the sequential patterns of stream data by making a tradeoff between the performance of increasingly updating algorithms and the difference of sequential patterns. The experiments for the increasingly updating algorithm IUS on the alarm data show that generally, as the size of incremental windows grows, the values of the speedup and the values of the difference will decrease and increase respectively. It is also shown experimentally that the incremental ratio determined by the TPD method does not monotonically increase or decrease but changes in a range between 20 and 30 percentage for the IUS algorithm.