Abstract
Rules are an important pattern in data mining, but existing approaches are limited to conjunctions of binary literals, fixed measures and counting based algorithms. Rules can be much more diverse, useful and interesting! This work introduces and solves the Generalised Rule Mining (GRM) problem, which abstracts rule mining, removes restrictions on the semantics of rules and redefines rule mining by functions on vectors. This also lends to an interesting geometric interpretation for rule mining. The GRM framework and algorithm allow new methods that are not possible with existing algorithms, can speed up existing methods and separate rule semantics from algorithmic considerations. The GRM algorithm scales linearly in the number of rules found and provides orders of magnitude speed up over fast candidate generation type approaches (in cases where these can be applied).
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References
Asuncion, D.N.A.: UCI machine learning repository (2007)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. In: ACM SIGMOD ICMD (1997)
Dunkel, B., Soparkar, N.: Data organization and access for efficient data mining. In: ICDE (1999)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD ICMD, ACM Press, New York (2000)
Li, W., Han, J., Pei, J.: Cmar: Accurate and efficient classification based on multiple class-association rules. In: ICDM 2001. IEEE Computer Society, Los Alamitos (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Knowledge Discovery and Data Mining, pp. 80–86 (1998)
Shenoy, P., Bhalotia, G., Haritsa, J.R., Bawa, M., Sudarshan, S., Shah, D.: Turbo-charging vertical mining of large databases. In: ACM SIGMOD ICMD (2000)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: ACM SIGMOD ICMD (1996)
Verhein, F., Chawla, S.: Geometrically inspired itemset mining. In: ICDM, pp. 655–666. IEEE Computer Society, Los Alamitos (2006)
Verhein, F., Chawla, S.: Using significant, positively and relatively class correlated rules for associative classification of imbalanced datasets. In: ICDM. IEEE Computer Society, Los Alamitos (2007)
Wang, J., Karypis, G.: Harmony: Efficiently mining the best rules for classification. In: SDM 2005 (2005)
Webb, G.I.: Discovering significant rules. In: ACM SIGKDD. ACM Press, New York (2006)
Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: SDM. SIAM, Philadelphia (2003)
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Verhein, F. (2010). Generalised Rule Mining. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12026-8_9
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DOI: https://doi.org/10.1007/978-3-642-12026-8_9
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