When to Update the Sequential Patterns of Stream Data?

Q. Zheng, K. Xu and S. Ma. When to Update the Sequential Patterns of Stream Data? Proc.
7th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Korea, LNAI 2637, pages 545-550, 2003.

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 incremental updating algorithm IUS on two data sets 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.
Keywords: stream data, incremental data mining, sequential patterns, difference measure, updating.

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