Breaking the barrier of transactions: mining inter-transaction association rules

Most of the previous studies on mining association rules are on mining intro-transaction associations, i.e., the associations among items within the same transaction, where the notion of the transaction could be the items bought by the same customer, the events happened on the same day, etc. In this study, we break the barrier of transactions and extend the scope of mining association rules from traditional intratransaction associations to inter-transaction associations. Mining inter-transaction associations poses more challenges on efficient processing than mining intra-transaction associations because the number of potential association rules becomes extremely large after the boundary of transactions is broken. In this study, we introduce the notion of inter-transaction association rule, define its measurements: support and confidence, and develop an efficient algorithm, FITI (an acronym for “First Intra Then Inter”), for mining inter-transaction associations. We compare FITI with EH-Apriori, the best algorithm in our previous proposal, and demonstrate a substantial performance gain of FITI over EH-

[1]  Laks V. S. Lakshmanan,et al.  Optimization of constrained frequent set queries with 2-variable constraints , 1999, SIGMOD '99.

[2]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

[3]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[4]  Raymond T. Ng,et al.  Very large data bases , 1994 .

[5]  Jiawei Han,et al.  Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes , 1997, KDD.

[6]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[7]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[8]  Giuseppe Psaila,et al.  A New SQL-like Operator for Mining Association Rules , 1996, VLDB.

[9]  Ramakrishnan Srikant,et al.  Mining Association Rules with Item Constraints , 1997, KDD.

[10]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

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

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

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

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

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