Abstract
Association rules are rules of the kind “70% of the customers who buy vine and cheese also buy grapes”. While the traditional field of application is market basket analysis, association rule mining has been applied to various fields since then, which has led to a number of important modifications and extensions. We discuss the most frequently applied approach that is central to many extensions, the Apriori algorithm, and briefly review some applications to other data types, well-known problems of rule evaluation via support and confidence, and extensions of or alternatives to the standard framework.
Keywords
Association Rules AprioriPreview
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References
- Agrawal R. and Srikant R. Fast Algorithms for mining association rules. Proc. Int. Conf. on Very Large Databases, 487–499, 1994Google Scholar
- Agrawal R. and Srikant R. Mining Sequential Patterns. Proc. Int. Conf. on Data Engineering, 3–14, 1995.Google Scholar
- Agrawal R., Mannila H., Srikant R., Toivonen H., Verkamo A. I. Fast Discovery of Association Rules. In: Advances in Knowledge Discovery and Data Mining, Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. (eds)., AAAI Press / The MIT Press, 307–328, 1996Google Scholar
- Aumann Y., Lindell, Y. A Statistical Theory for Quantitative Association Rules, Journal of Intelligent Information Systems, 20(3):255–283, 2003CrossRefGoogle Scholar
- Bayardo R.J., Agrawal R. Mining the Most Interesting Rules. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 145–154, 1999Google Scholar
- Bayardo R.J., Agrawal R., Gunopulos D. Constrained-Based Rule Mining in Large, Dense Databases. Proc. 15th Int. Conf. on Data Engineering, 188–197, 1999Google Scholar
- Berzal F., Blanco L., Sanchez D., Vila M.A. A New Framework to Assess Association Rules. Proc. Symp. on Intelligent Data Analysis, LNCS 2189, 95–104, Springer, 2001Google Scholar
- Bolton R., Hand D.J., Adams, N.M. Determining Hit Rate in Pattern Search, Proc. Pattern Detection and Discovery, LNAI 2447, 36–48, Springer, 2002Google Scholar
- Borgelt C, Berthold M. Mining Molecular Fragments: Finding Relevant Substructures of Molecules, Proc. Int. Conf. on Data Mining, 51–58, 2002Google Scholar
- Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees. Chapman & Hall, New York, 1984.Google Scholar
- Brijs T., Swinnen G., Vanhoof K. and Wets G. Using Association Rules for Product Assortment Decisions: A Case Study. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 254–260, 1999Google Scholar
- Brin S., Motwani R., and Silverstein C. Beyond market baskets: Generalizing association rules to correlations. Proc. ACM SIGMOD Int. Conf. Management of Data, 265–276, 1997Google Scholar
- Brin S., Motwani R., Ullman, J.D., Tsur, S. Dynamic itemset counting and implication rules for market basket data. SIGMOD Record 26(2):255–264, 1997CrossRefGoogle Scholar
- Castelo R., Feelders A., Siebes A. Mambo: Discovering Association Rules. Proc. Symp. on Intelligent Data Analysis. LNCS 2189, 289–298, Springer, 2001Google Scholar
- Clark P., Boswell R. Rule Induction with CN2: Some recent improvements. In Proc. European Working Session on Learning EWSL-91, 151–163, 1991Google Scholar
- Cohen E., Datar M., Fujiwara S., Gionis A., Indyk P., Motwani R., Ullman J., Yang C. Finding Interesting Associations without Support Pruning, IEEE Transaction on Knowledge Discovery 13(1):64–78, 2001CrossRefGoogle Scholar
- Dehaspe L., Toivonen H. Discovery of Frequent Datalog Patterns. Data Mining and Knowledge Discovery, 3(1):7–38, 1999CrossRefGoogle Scholar
- Delgado M., Martin-Bautista M.J., Sanchez D., Vila M.A. Mining Text Data: Features and Patterns. Proc. Pattern Detection and Discovery, LNAI 2447, 140–153, Springer, 2002Google Scholar
- Dong G., Li J. Interestingness of Discovered Association Rules in Terms of Neighbourhood-Based Unexpectedness. Proc. Pacific Asia Conf. on Knowledge Discovery in Databases, LNAI 1394, 72–86, 1998Google Scholar
- Friedman J.H., Fisher N.I. Bump Hunting in High-Dimensional Data. Statistics and Computing 9(2), 123–143, 1999CrossRefGoogle Scholar
- Gamberger D., Lavrac N., Jovanoski, V. High Confidence Association Rules for Medical Diagnosis, Proc. of Intelligent Data Analysis in Medical Applications (IDAMAP), 42–51, 1999Google Scholar
- Goethals B., Van den Bussche, J. On Supporting Interactive Association Rule Mining, Proc. Int. Conf. Data Warehousing and Knowledge Discovery, LNCS 1874, 307–316, Springer, 2000Google Scholar
- Han J., Fu Y. Discovery of Multiple-Level Association Rules from Large Databases Proc. Int. Conf. on Very Large Databases, 420–431, 1995Google Scholar
- Hilderman R. J. and Hamilton H. J. Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers, 2001Google Scholar
- Höppner F, Klawonn F. Finding Informative Rules in Interval Sequences. Intelligent Data Analysis, 6(3):237–256, 2002Google Scholar
- Höppner F. Discovery of Core Episodes from Sequences. Proc. Pattern Detection and Discovery, LNAI2447, 199–213, Springer, 2002Google Scholar
- Klemettinen M., Mannila H., Ronkainen P., Toivonen, H. and Verkamo A. I. Finding Interesting Rules from Large Sets of Discovered Association Rules. Proc. Int. Conf. on Information and Knowledge Managament, 401–407, 1994.Google Scholar
- Koperski K., Han J. Discovery of Spatial Association Rules in Geographic Information Databases. Proc. Int. Symp Advances in Spatial Databases, LNCS 951, 47–66, 1995Google Scholar
- Kryszkiewicz M. Concise Representation of Frequent Patterns based on Disjunction-free Generators. Proc. Int. Conf. on Data Mining, 305–312, 2001.Google Scholar
- Lee C. H., Lin C. R., Chen M. S. On Mining General Temporal Association Rules in a Publication Database. Proc. Int. Conf. on Data Mining, 337–344, 2001Google Scholar
- Li J., Zhang X., Dong G., Ramamohanarao K., Sun Q. Efficient Mining of High Confidence Association Rules without Support Thresholds. Proc. Principles of Data Mining and Knowledge Discovery, 406–411, 1999Google Scholar
- Liu B., Hsu W., Ma Y. Pruning and Summarizing the Discovered Associations. Proc. ACM SIGKDD Conf. Knowledge Discovery and Data Mining, 125–134, 1999Google Scholar
- Mannila H., Toivonen H. Multiple uses of frequent sets and condensed representations. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 189–194, 1996Google Scholar
- Mannila H., Toivonen H., Verkamo A. I. Discovery of Frequent Episodes in Event Sequences. Data Mining and Knowledge Discovery, 1(3):259–289, 1997CrossRefGoogle Scholar
- Miller R. J., Yang Y. Association Rules over Interval Data. Proc. Int. Conf. on Management of Data, 452–461, 1997Google Scholar
- Mitchell T, Machine Learning, McGraw-Hill, 1997Google Scholar
- Mobasher B., Dai H., Luo T, Nakagawa, M. Discovery and Evaluation of Aggregate Usage Profile for Web Personalization, Data Mining and Knowledge Discovery 6:61–82, 2002MathSciNetCrossRefGoogle Scholar
- Padmanabhan B., Tuzhilin A. A Belief-Driven Method for Discovering Unexpected Patterns. Int. Conf. on Knowledge Discovery and Data Mining, 94–100, 1998Google Scholar
- Pasquier N., Bastide Y., Taouil R., Lakhal L. Efficient Mining of association rules using closed itemset lattices. Information Systems, 24(1):25–46, 1999MathSciNetCrossRefGoogle Scholar
- Piatetsky-Shapiro, G. Discovery, Analysis, and Presentation of Strong Rules. Proc. Knowledge Discovery in Databases, 229–248, 1991Google Scholar
- Smyth P. and Goodman, R.M. An Information Theoretic Approach to Rule Induction from Databases. IEEE Trans. Knowledge Discovery and Data Engineering, 4(4):301–316, 1992CrossRefGoogle Scholar
- Srikant R., Agrawal R. Mining Generalized Association Rules. Proc. Int. Conf. on Very Large Databases, 407–419, 1995Google Scholar
- Srikant R., Agrawal R. Mining Quantitative Association Rules in Large Relational Tables. Proc. ACM SIGMOD Conf. on Management of Data, 1–12, 1996Google Scholar
- Srikant R., Vu Q., Agrawal R. Mining Association Rules with Constraints. Proc. Int. Conf. Knowledge Discovery and Data Mining, 66–73, 1997Google Scholar
- Tan P. N., Kumar V. Selecting the Right Interestingness Measure for Association Patterns, Proc. ACM SIGKDD Conf. Knowledge Discovery and Data Mining, 32–41, 2002Google Scholar
- Tsoukatos, I. and Gunopulos, D. Efficient Mining of Spatiotemporal Patterns. Proc. Int. Symp. Spatial and Temporal Databases, LNCS 2121, pages 425–442, 2001Google Scholar
- Wang J., Han J., Pei J. CLOSET+: Searching for the Best Strategies for mining Frequent Closed Itemsets, Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 236–245, 2003Google Scholar
- Webb G.I. Efficient Search for Association Rules. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 99–107, 2000Google Scholar
- Webb G.I. Discovering Associations with numeric variables. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 383–388, 2001Google Scholar
- Zaki M. J. Generating non-redundant Association Rules. In Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 34–43, 2000Google Scholar
- Zaki M. J. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 42(1):31–60, 2001MATHCrossRefGoogle Scholar
- Zelenko D. Optimizing Disjunctive Association Rules. Proc. of Int. Conf. on Principles of Data Mining and Knowledge Discovery, LNAI1704, 204–213, 1999Google Scholar
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