论文信息 - The PSP Approach for Mining Sequential Patterns

The PSP Approach for Mining Sequential Patterns

In this paper, we present an approach, called PSP, for mining sequential patterns embedded in a database. Close to the problem of discovering association rules, mining sequential patterns requires handling time constraints. Originally introduced in [3], the issue is addressed by the GSP approach [10]. Our proposal resumes the general principles of GSP but it makes use of a different intermediary data structure which is proved to be more efficient than in GSP.

[1] Shamkant B. Navathe,et al. An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[2] Ramakrishnan Srikant,et al. Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[3] Rajeev Motwani,et al. Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[4] Hannu Toivonen,et al. Sampling Large Databases for Association Rules , 1996, VLDB.

[5] Andreas Mueller,et al. Fast sequential and parallel algorithms for association rule mining: a comparison , 1995 .

[6] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[7] Ramakrishnan Srikant,et al. Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.