Multi-dimensional, inter-transaction association rules extend the traditional association rules to describe more general associations among items with multiple properties cross transactions. “After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away” is an example of such rules. Since the number of potential inter-transaction association rules tends to be extremely large, mining inter-transaction associations poses more challenges on efficient processing than mining intra-transaction associations. In order to make such association mining truly practical and computationally tractable, in this study, we present a template model to help users declare the interesting inter-transaction associations to be mined. With the guidance of templates, several optimization techniques are devised to speed up the discovery of inter-transaction association rules. We show, through a series of experiments, that these optimization techniques can yield significant performance benefits.

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AIS93   Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States

AS94   R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. 20th intl. Conf. Very Large Data Bases, pages 478-499, Santiago, Chile, September 1994.

AS96   R. Agrawal and J.C. Sharer. Parallel mining of association rules: Design, implementation and experience. Technical Report IBM Research Report RJ 10004, 1996.

BP9?   Elena Baralis , Giuseppe Psaila, Designing Templates for Mining Association Rules, Journal of Intelligent Information Systems, v.9 n.1, p.7-32, July/Aug. 1997

BWJ96   Claudio Bettini , X. Sean Wang , Sushil Jajodia, Testing complex temporal relationships involving multiple granularities and its application to data mining (extended abstract), Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, p.68-78, June 04-06, 1996, Montreal, Quebec, Canada

BWJ98   C. Bettini, X. Wang, and S. Jajodia. Mining temporal relationships with multiple granularities in time sequences. Da~a Engineering, 21(1):32-38, March 1998.

DLM+98   G. Das, K.-I. Lin, H. Mannila, G. Renganathan, and P. Smyth. R. ule discovery from time series. In Proc. of the second ~nternationai Conference on Knowledge Discovery and Data Mining, pages 16-22, New York, USA, August 1998.

HF95   J. Han and Y. Fu. Meta-rule-guided mining of association rules in relational databases. In Proc. I st Intl. Workshop on Integration of Knowledge Discovery with Deductive and Object-Oriented Databases, pages 39-46, Singapore, December 1995.

LHF98   H. Lu, J. Han, and L. Feng. Stock movement prediction and n-dimensional inter-transaction association rules. In Proe. A CM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Dis. covery, pages 12:1-12:7, Seattle, Washington, June 1998.

MPC96   R. Meo, G. Psaila, and S. Ceri. A new sql-like operator for mining association rules. In Proc. 22th Intl. Conf. Very Large Data Bases, pages 122-133, Mumbai, India, September 1996.

MT96   H. Mannila and H. Toivonen. Discovering generalized episodes using minimal occurrences. In Proc. $nd Intl. Conf. Knowledge Discovery and Data Mining, pages 146-151, Portland, Oregon, August 1996.

MTV97   H. Mannila, H. Toivonen, and A. Verkamo. Discovering frequent episodes in event sequences. Technical Report Department of Computer Science, Report C- 1997-15, February 1997.

NLHP98   Raymond T. Ng , Laks V. S. Lakshmanan , Jiawei Han , Alex Pang, Exploratory mining and pruning optimizations of constrained associations rules, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.13-24, June 01-04, 1998, Seattle, Washington, United States

SVA97   R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In Proc. 3rd Intl. Conf. Knowledge Discovery and Data Mining, pages 67-73, Newport Beach, California, August 1997.

TUA+98   Dick Tsur , Jeffrey D. Ullman , Serge Abiteboul , Chris Clifton , Rajeev Motwani , Svetlozar Nestorov , Arnon Rosenthal, Query flocks: a generalization of association-rule mining, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.1-12, June 01-04, 1998, Seattle, Washington, United States

Hongjun Lu , Ling Feng , Jiawei Han, Beyond intratransaction association analysis: mining multidimensional intertransaction association rules, ACM Transactions on Information Systems (TOIS), v.18 n.4, p.423-454, Oct. 2000

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