Abstract
Mining association rules associates events that took place together. In market basket analysis, these discovered rules associate items purchased together. Items that are not part of a transaction are not considered. In other words, typical association rules do not take into account items that are part of the domain but that are not together part of a transaction. Association rules are based on frequencies and count the transactions where items occur together. However, counting absences of items is prohibitive if the number of possible items is very large, which is typically the case. Nonetheless, knowing the relationship between the absence of an item and the presence of another can be very important in some applications. These rules are called negative association rules. We review current approaches for mining negative association rules and we discuss limitations and future research directions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Charu C. Aggarwal and Philip S. Yu. Mining large itemsets for association rules. IEEE Data Eng. Bull., 21(1):23–31, 1998.
Charu C. Aggarwal and Philip S. Yu. A new framework for itemset generation. In Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS '98, pages 18–24, 1998.
C. C. Aggarwal and P. S. Yu. Mining associations with the collective strength approach. IEEE Trans. on Knowl. and Data Eng., 13(6):863–873, November 2001.
Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. Mining association rules between sets of items in large databases. In Proc. of SIGMOD, pages 207–216, 1993.
M-L Antonie and Osmar R Zaïane. Text document categorization by term association. In Proc. of ICDM, pages 19–26, 2002.
M-L Antonie and Osmar R Zaïane. An associative classifier based on positive and negative rules. In Proc. of DMKD, pages 64–69, 2004.
Maria-Luiza Antonie and Osmar R Zaïane. Mining positive and negative association rules: An approach for confined rules. In Proc. of PKDD, pages 27–38, 2004.
Sergey Brin, Rajeev Motwani, and Craig Silverstein. Beyond market basket: Generalizing association rules to correlations. In Proc. SIGMOD, pages 265–276, 1997.
Sergey Brin, Rajeev Motwani, Jeffrey D Ullman, and Shalom Tsur. Dynamic itemset counting and implication rules for market basket data. In Proc. of SIGMOD, pages 255–264, 1997.
Chris Cornelis, Peng Yan, Xing Zhang, and Guoqing Chen. Mining positive and negative association rules from large databases. In Proc. of CIS, pages 1–6, 2006.
Bart Goethals and Mohammed J Zaki. FIMI 2003: Workshop on frequent itemset mining implementations. In Third IEEE International Conference on Data Mining Workshop on Frequent Itemset Mining Implementations, pages 1–13, 2003.
Wilhelmiina Hamalainen. Efficient discovery of the top-k optimal dependency rules with fisher’s exact test of significance. In Proc. of ICDM, pages 196–205, 2010.
Wilhelmiina Hamalainen. Kingfisher: an efficient algorithm for searching for both positive and negative dependency rules with statistical significance measures. Knowl. Inf. Syst., 32(2):383–414, 2012.
Jiawei Han, Jian Pei, and Yiwen Yin. Mining frequent patterns without candidate generation. In Proc. of SIGMOD, pages 1–12, 2000.
Yun Sing Koh and Russel Pears. Efficiently finding negative association rules without support threshold. In Proc. of Australian AI, pages 710–714, 2007.
Wenmin Li, Jiawei Han, and Jian Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. In Proc. of ICDM, pages 369–376, 2001.
Bing Liu, Wynne Hsu, and Yiming Ma. Integrating classification and association rule mining. In Proc. of SIGKDD, pages 80–86, 1998.
Paul David McNicholas, Thomas Brendan Murphy, and M. O’Regan. Standardising the lift of an association rule. Comput. Stat. Data Anal., 52(10):4712–4721, 2008.
Ashok Savasere, Edward Omiecinski, and Shamkant Navathe. Mining for strong negative associations in a large database of customer transactions. In Proc. of ICDE, pages 494–502, 1998.
Wei-Guang Teng, Ming-Jyh Hsieh, and Ming-Syan Chen. On the mining of substitution rules for statistically dependent items. In Proc. of ICDM, pages 442–449, 2002.
Wei-Guang Teng, Ming-Jyh Hsieh, and Ming-Syan Chen. A statistical framework for mining substitution rules. Knowl. Inf. Syst., 7(2):158–178, 2005.
D. R. Thiruvady and G. I. Webb. Mining negative rules using grd. In Proc. of PAKDD, pages 161–165, 2004.
Hao Wang, Xing Zhang, and Guoqing Chen. Mining a complete set of both positive and negative association rules from large databases. In Proc. of PAKDD, pages 777–784, 2008.
Xindong Wu, Chengqi Zhang, and Shichao Zhang. Efficient mining of both positive and negative association rules. ACM Trans. on Inf. Syst., 22(3):381–405, 2004.
Xiaohui Yuan, Bill P Buckles, Zhaoshan Yuan, and Jian Zhang. Mining negative association rules. In Proc. of ISCC, pages 623–628, 2002.
Mohammed J Zaki. Parallel and distributed association mining: A survey. IEEE Concurrency: Special Issue on Parallel Mechanisms for Data Mining, 7(4):14–25, 1999.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Antonie, L., Li, J., Zaiane, O. (2014). Negative Association Rules. In: Aggarwal, C., Han, J. (eds) Frequent Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-07821-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07820-5
Online ISBN: 978-3-319-07821-2
eBook Packages: Computer ScienceComputer Science (R0)