Using a projection-based approach to mine frequent inter-transaction patterns

In this paper, we propose an algorithm called PITP-Miner that utilizes a projection based approach to mine frequent inter-transaction patterns efficiently. The algorithm only searches for local frequent items in a projected database that stores potential local inter-transaction items and partitions the database into a set of smaller databases recursively. In addition, two pruning strategies are designed to further condense the partitioned databases and thus accelerate the algorithm. Our experiment results demonstrate that the proposed PITP-Miner algorithm outperforms the ITP-Miner and FITI algorithms in most cases.

[1]  James Nga-Kwok Liu,et al.  Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data , 2001, Data Knowl. Eng..

[2]  Anthony K. H. Tung,et al.  Efficient Mining of Intertransaction Association Rules , 2003, IEEE Trans. Knowl. Data Eng..

[3]  Anthony J. T. Lee,et al.  An efficient algorithm for mining frequent inter-transaction patterns , 2007, Inf. Sci..

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

[5]  Qing Li,et al.  From intra-transaction to generalized inter-transaction: Landscaping multidimensional contexts in association rule mining , 2005, Inf. Sci..

[6]  Anthony J. T. Lee,et al.  Mining inter-sequence patterns , 2009, Expert Syst. Appl..

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

[8]  Anthony J. T. Lee,et al.  Mining association rules with multi-dimensional constraints , 2006, J. Syst. Softw..

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

[10]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[11]  Hongjun Lu,et al.  Beyond intratransaction association analysis: mining multidimensional intertransaction association rules , 2000, TOIS.

[12]  Hongjun Lu,et al.  A template model for multidimensional inter-transactional association rules , 2002, The VLDB Journal.

[13]  Hongjun Lu,et al.  Stock movement prediction and N-dimensional inter-transaction association rules , 1998, SIGMOD 1998.

[14]  Jianyong Wang,et al.  Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[15]  Charu C. Aggarwal,et al.  A Tree Projection Algorithm for Generation of Frequent Item Sets , 2001, J. Parallel Distributed Comput..

[16]  Anthony J. T. Lee,et al.  Mining spatial association rules in image databases , 2007, Inf. Sci..