Mining Quantitative Association Rules in a Large Database of Sales Transactions

Previous studies on mining association rules focus on discovering associations among items without considering the relationships between items and their purchased quantities. However, exploring associations among items associated with their purchased quantities may discover information useful to improve the quality of business decisions. In this paper, we investigate the issue of mining quantitative association rules in a large database of sales transactions. When purchased quantities are considered, the supports of items associated with their purchased quantities may decrease drastically, and the number of potentially interesting association rules discovered will be few. In order to discover more potentially interesting rules, we present two partition algorithms to partition all the possible quantities into intervals for each item. We also propose an efficient mechanism to discover all the large itemsets from the partitioned data. Experimental results show that by our approach, the total execution time can be reduced significantly. Moreover, the potentially interesting rule discovered from the partitioned data can be considered to be a kind of generalized association rule. The generalized association rule is useful in marketing, business management and decision making, especially when the information from the rules generated from the original data is limited.

[1]  Kyuseok Shim,et al.  Mining optimized association rules with categorical and numeric attributes , 1998, Proceedings 14th International Conference on Data Engineering.

[2]  Philip S. Yu,et al.  Using a Hash-Based Method with Transaction Trimming for Mining Association Rules , 1997, IEEE Trans. Knowl. Data Eng..

[3]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[4]  Jennifer Widom,et al.  Clustering association rules , 1997, Proceedings 13th International Conference on Data Engineering.

[5]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

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

[7]  Jiawei Han,et al.  An attribute-oriented approach for learning classification rules from relational databases , 1990, [1990] Proceedings. Sixth International Conference on Data Engineering.

[8]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

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

[10]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[11]  Yasuhiko Morimoto,et al.  Mining optimized association rules for numeric attributes , 1996, J. Comput. Syst. Sci..

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

[13]  Tomasz Imielinski,et al.  An Interval Classifier for Database Mining Applications , 1992, VLDB.

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

[15]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.

[16]  Xiaohua Hu,et al.  Mining knowledge rules from databases: a rough set approach , 1996, Proceedings of the Twelfth International Conference on Data Engineering.