Abstract
In this paper, we present an analysis of behavior of objective rule evaluation indices on classification rule sets using Pearson product-moment correlation coefficients between each index. To support data mining post-processing, which is one of important procedures in a data mining process, at least 40 indices are proposed to find out valuable knowledge. However, their behavior have never been clearly articulated. Therefore, we carried out a correlation analysis between each objective rule evaluation indices. In this analysis, we calculated average values of each index using bootstrap method on 32 classification rule sets learned with information gain ratio. Then, we found the following relationships based on the correlation coefficient values: similar pairs, discrepant pairs, and independent indices. With regarding to this result, we discuss about relative functional relationships between each group of objective indices.
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References
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measure of Interest. Kluwer Academic Publishers, Dordrecht (2001)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of International Conference on Knowledge Discovery and Data Mining KDD 2002, pp. 32–41 (2002)
Yao, Y.Y., Zhong, N.: An analysis of quantitative measures associated with rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 479–488. Springer, Heidelberg (1999)
Ohsaki, M., Abe, H., Yokoi, H., Tsumoto, S., Yamaguchi, T.: Evaluation of rule interestingness measures in medical knowledge discovery in databases. Artificial Intelligence in Medicine 41(3), 177–196 (2007)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Explora: A Multipattern and Multistrategy Discovery Assistant. Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI/MIT Press, California (1996)
Ali, K., Manganaris, S., Srikant, R.: Partial classification using association rules. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining KDD 1997, pp. 115–118 (1997)
Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 255–264 (1997)
Rijsbergen, C.: Information retrieval, ch. 7 (1979), http://www.dcs.gla.ac.uk/Keith/Chapter.7/Ch.7.html
Gray, B., Orlowska, M.E.: CCAIIA: Clustering categorical attributes into interesting association rules. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 132–143. Springer, Heidelberg (1998)
Hamilton, H.J., Shan, N., Ziarko, W.: Machine learning of credible classifications. In: Australian Conf. on Artificial Intelligence AI 1997, pp. 330–339 (1997)
Goodman, L.A., Kruskal, W.H.: Measures of association for cross classifications. Springer Series in Statistics, vol. 1. Springer, Heidelberg (1979)
Smyth, P., Goodman, R.M.: Rule induction using information theory. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 159–176. AAAI/MIT Press, Cambridge (1991)
Ohsaki, M., Kitaguchi, S., Kume, S., Yokoi, H., Yamaguchi, T.: Evaluation of rule interestingness measures with a clinical dataset on hepatitis. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 362–373. Springer, Heidelberg (2004)
Piatetsky-Shapiro, G.: Discovery, analysis and presentation of strong rules. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press (1991)
Gago, P., Bento, C.: A metric for selection of the most promising rules. In: European Conference on the Principles of Data Mining and Knowledge Discovery PKDD 1998, pp. 19–27 (1998)
Zhong, N., Yao, Y.Y., Ohshima, M.: Peculiarity oriented multi-database mining. IEEE Transactions on Knowledge and Data Engineering 15(4), 952–960 (2003)
Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: The Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
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Abe, H., Tsumoto, S. (2008). Analyzing Behavior of Objective Rule Evaluation Indices Based on Pearson Product-Moment Correlation Coefficient. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_9
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DOI: https://doi.org/10.1007/978-3-540-68123-6_9
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