Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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Mining sequential patterns is to discover sequential purchasing behaviors of most customers from a large amount of customer transactions. The previous approaches for mining sequential patterns need to repeatedly scan the large database, and take a large amount of computation time to find frequent sequences, which are very time consuming. In this paper, we present an algorithm SSLP to find sequential patterns, which can significantly reduce the number of the database scans. The experimental results show that our algorithms are more efficient than the other algorithms.