Abstract
In data mining post-processing, which is one of important procedures in a data mining process, at least 39 metrics have been proposed to find out valuable knowledge. However, their functional properties have never been clearly articulated under the same condition. Therefore, we carried out a correlation analysis of functional properties between each objective rule evaluation indices on classification rule sets using correlation coefficients between each index. In this analysis, we calculated average values of each index using bootstrap method on 34 classification rule sets learned based on information gain ratio. Then, we found the following relationships based on 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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
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)
Freitas, A.A.: On rule interestingness measures. Knowledge-Based Systtems 12(5-6), 309–315 (1999)
Vaillant, B., Lenca, P., Lallich, S.: A clustering of interestingness measures. In: Proceedings of the Discovery Science 2004, pp. 290–297 (2004)
Huynh, X.H., Guillet, F., Briand, H.: A data analysis approach for evaluating the behavior of interestingness measures. In: Proceeding of the Discovery Science 2005, pp. 330–337 (2005)
Blanchard, J., Guillet, F., Gras, R., Briand, H.: Using information-theoretic measures to assess association rule interestingness. In: Proceedings of the fifth IEEE International Conference on Data Mining ICDM 2005, pp. 66–73. IEEE Computer Society, Los Alamitos (2005)
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)
Explora: A Multipattern and Multistrategy Discovery Assistant. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) 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 (1991)
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, Department of Information and Computer Science. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: The Fifteenth International Conference on Machine Learning, pp. 144–151 (1998)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abe, H., Tsumoto, S. (2008). Analyzing Correlation Coefficients of Objective Rule Evaluation Indices on Classification Rules. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-79721-0_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
Online ISBN: 978-3-540-79721-0
eBook Packages: Computer ScienceComputer Science (R0)