Mining Frequent Ordered Patterns

Mining frequent patterns has been studied popularly in data mining research. All of previous studies assume that items in a pattern are unordered. However, the order existing between items must be considered in some applications. In this paper, we first give the formal model of ordered patterns and discuss the problem of mining frequent ordered patterns. Base on our analyses, we present two efficient algorithms for mining frequent ordered patterns. We also present results of applying these algorithms to a synthetic data set, which show the effectiveness of our algorithms.

[1]  Rajeev Motwani,et al.  Beyond market baskets: generalizing association rules to correlations , 1997, SIGMOD '97.

[2]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[3]  Mohammed J. Zaki Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[4]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[5]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[6]  Walid G. Aref Mining Association Rules in Large Databases , 2004 .

[7]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

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

[9]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.