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PAKDD
2007
ACM

Intelligent Sequential Mining Via Alignment: Optimization Techniques for Very Large DB

11 years 11 months ago
Intelligent Sequential Mining Via Alignment: Optimization Techniques for Very Large DB
The shear volume of the results in traditional support based frequent sequential pattern mining methods has led to increasing interest in new intelligent mining methods to find more meaningful and compact results. One such approach is the consensus sequential pattern mining method based on sequence alignment, which has been successfully applied to various areas. However, the current approach to consensus sequential pattern mining has quadratic run time with respect to the database size limiting its application to very large databases. In this paper, we introduce two optimization techniques to reduce the running time significantly. First, we determine the theoretical bound for precision of the proximity matrix and reduce the time spent on calculating the full matrix. Second, we use a sample based iterative clustering method which allows us to use a much faster k-means clustering method with only a minor increase in memory consumption with negligible loss in accuracy.
Hye-Chung Kum, Joong Hyuk Chang, Wei Wang 0010
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where PAKDD
Authors Hye-Chung Kum, Joong Hyuk Chang, Wei Wang 0010
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