ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fast and Exact Out-of-Core K-Means Clustering
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
Data Mining and Knowledge Discovery
Benchmarking the effectiveness of sequential pattern mining methods
Data & Knowledge Engineering
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
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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.