Abstract
In order to obtain valuable knowledge from stored data on database systems, rule mining is considered as one of the usable mining method. However, almost current rule mining algorithms only use primary difference of a criterion to select attribute-value pairs to obtain a rule set to a given dataset. In this paper, we implemented a rule generation method based on secondary differences of two criteria. Then, we performed a case study using UCI common datasets. With regarding to the result, we compared the accuracies of rule sets learned by our algorithm with that of three representative algorithms.
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References
Furnkranz, J., Flach, P.A.: ROC ‘n’ rule learning: towards a better understanding of covering algorithms. Machine Learning 58(1), 39–77 (2005)
Tsumoto, S.: Accuracy and coverage in rough set rule induction. In: 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2006)
Furnkranz, J.: Separate-and-Conquer Rule Learning. Artificial Intelligence Review 13(1), 3–54 (1999)
Michalski, R.S.: On the QuasiMinimal Solution of the Covering Problem. In: Proceedings of the 5th International Symposium on Information Processing (FCIP 1969) (Switching Circuits), vol. A3, pp. 125–128 (1969)
Mitchell, T.M.: Generalization as Search. Artificial Intelligence 18(2), 203–226 (1982)
Quinlan, J.R.: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1992)
Jovanoski, V., Lavrac, N.: Classification Rule Learning with APRIORI-C. In: Brazdil, P.B., Jorge, A.M. (eds.) EPIA 2001. LNCS (LNAI), vol. 2258, pp. 44–51. Springer, Heidelberg (2001)
Booker, L.B., Holland, J.H., Goldberg, D.E.: Classifier Systems and Genetic Algorithms. Artificial Intelligence 40, 235–282 (1989)
Goodman, R.M., Smyth, P.: The induction of probabilistic rule sets—the Itrule algorithm. In: Proceedings of the sixth international workshop on Machine Learning, pp. 129–132 (1989)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: The Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
COIN Project, http://coin.sourceforge.jp/
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11, 63–91 (1993)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
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Abe, H., Tsumoto, S. (2008). Implementing a Rule Generation Method Based on Secondary Differences of Two Criteria. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_30
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DOI: https://doi.org/10.1007/978-3-540-88425-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88423-1
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