Sequential Pattern Mining in Multiple Streams

Authors:
Gong Chen;Xindong Wu;Xingquan Zhu
Affiliations:
University of Vermont;University of Vermont;University of Vermont
Venue:
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Year:
2005

Citing 1
Cited 11

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Abstract

In this paper, we deal with mining sequential patterns in multiple data streams. Building on a state-of-the-art sequential pattern mining algorithm PrefixSpan for mining transaction databases, we propose MILE鹿, an efficient algorithm to facilitate the mining process. MILE recursively utilizes the knowledge of existing patterns to avoid redundant data scanning, and can therefore effectively speed up the new patterns' discovery process. Another unique feature of MILE is that it can incorporate some prior knowledge of the data distribution in data streams into the mining process to further improve the performance. Extensive empirical results show thatMILE is significantly faster than PrefixSpan. As MILE consumes more memory than PrefixSpan, we also present a solution to balance the memory usage and time efficiency in memory constrained environments.