Definition
Let \({\cal I}\) be a set of items, where each item is a literal. A transaction \(T \subseteq {\cal I}\) is a subset of \({\cal I}\). Association rules are defined on a set of transactions \({\cal T}\).
An association rule R is in the form of X → Y , where X and Y are two sets of items, that is, \(X,Y \subseteq {\cal I}\). R is associated with two measures, the support sup(R) and the confidence conf (R). The support sup(R) is the probability that X appears in a transaction in \({\cal T}\). The confidence conf (R) is the conditional probability that when X appears in a transaction, Y also appears.
Historical Background
The concept of association rules were firstly proposed by Agrawal et al. [1] for market basket analysis. A well known illustrative example of association rules is “Diaper → Beer” which can be explained by the fact that, when dads buy diapers for their babies, they also buy beer at the same time for their weekends game watching.
Apriori, an efficient algorithm...
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
Recommended Reading
Agrawal R., Imielinski T., and Swami A. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1993, pp. 207–216.
Agrawal R., Mannila H., Srikant R., Toivonen H., and Verkamo A.I. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.). AAAI/MIT, Menlo Park, CA/Cambridge, MA, 1996, pp. 307–328.
Agrawal R. and Srikant R. Fast algorithms for mining association rules. In Proc. 20th Int. Conf. on Very Large Data Bases, 1994, pp. 487–499.
Agrawal R. and Srikant R. Mining sequential patterns. In Proc. 11th Int. Conf. on Data Engineering, 1995, pp. 3–14.
Brin S., Motwani R., and Silverstein C. Beyond market basket: generalizing association rules to correlations. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1997, pp. 265–276.
Dong G. and Li J. Efficient mining of emerging patterns: discovering trends and differences. In Proc. 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 1999, pp. 43–52.
Han J. and Fu Y. Discovery of multiple-level association rules from large databases. In Proc. 21th Int. Conf. on Very Large Data Bases, 1995, pp. 420–431.
Han J., Pei J., and Yin Y. Mining frequent patterns without candidate generation. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2000, pp. 1–12.
Koperski K. and Han J. Discovery of spatial association rules in geographic information databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1995, pp. 47–66.
Li W., Han J., and pei J. CMAR: accurate and efficient classification based on multiple class-association rules. In Proc. 2001 IEEE Int. Conf. on Data Mining, 2001, pp. 369–376.
Liu B., Hsu W., and Ma Y. Discovering the set of fundamental rule changes. In Proc. 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2001, pp. 335–340.
Lu H., Han J., and Feng L. Stock movement and n-dimensional inter-transaction association rules. In Proc. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 1998, pp. 1201–1217.
Mannila H., Toivonen H., and Verkamo A.I. Efficient algorithms for discovering association rules. In Proc. AAAI 1994 Workshop Knowledge Discovery in Databases, 1994, pp. 181–192.
Özden B., Ramaswamy S., and Silberschatz A. Cyclic association rules. In Proc. 14th Int. Conf. on Data Engineering, 1998, pp. 412–421.
Pasquier N., Bastide Y., Taouil R., and Lakhal L. Discovering frequent closed itemsets for association rules. In Proc. 7th Int. Conf. on Database Theory, 1999, pp. 398–416.
Pei J., Han J., Lu H., Nishio S., Tang S., and Yang D. H-Mine: hyper-structure mining of frequent patterns in large databases. In Proc. 2001 IEEE Int. Conf. on Data Mining, 2001, pp. 441–448.
Pei J., Han J., and Mao R. CLOSET: an efficient algorithm for mining frequent closed itemsets. In Proc. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000, pp. 11–20.
Piatetsky-Shapiro G. Discovery, analysis, and presentation of strong rules. In Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. Frawley (eds.). AAAI/MIT, Menlo Park, CA/Cambridge, MA, 1991, pp. 229–238.
Savasere A., Omiecinski E., and Navathe S. Mining for strong negative associations in a large database of customer transactions. In Proc. 14th Int. Conf. on Data Engineering, 1998, pp. 494–502.
Srikant R. and Agrawal R. Mining generalized association rules. In Proc. 21th Int. Conf. on Very Large Data Bases, 1995, pp. 407–419.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
Pei, J. (2009). Association Rules. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_26
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_26
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering