Summary
In this paper, we present an evaluation of learning algorithms of a rule evaluation support method with rule evaluation models based on objective indices for data mining post-processing. Post-processing of mined results is one of the key processes in a data mining process. However, it is difficult for human experts to evaluate several thousands of rules from a large dataset with noises for finding out reraly included valuable rules. To reduce the costs in such rule evaluation task, we have developed the rule evaluation support method with rule evaluation models which learn from a dataset. This dataset comprises objective indices for mined classification rules and evaluations by a human expert for each rule. To evaluate performances of learning algorithms for constructing the rule evaluation models, we have done a case study on the meningitis data mining as an actual problem. Furthermore, we have also evaluated our method with twelve rule sets obtained from twelve UCI datasets. With regard to these results, we show the availability of our rule evaluation support method for human experts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ali, K., Manganaris, S., Srikant, R.: Partial Classification Using Association Rules. Proc. of Int. Conf. on Knowledge Discovery and Data Mining KDD-1997 (1997) 115–118
Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. Proc. of ACM SIGMOD Int. Conf. on Management of Data (1997) 255–264
Frank, E., Wang, Y., Inglis, S., Holmes, G., and Witten, I. H.: Using model trees for classification. Machine Learning, Vol.32, No.1 (1998) 63–76
Frank, E, Witten, I. H., Generating accurate rule sets without global optimization. Proc. of the Fifteenth International Conference on Machine Learning, (1998) 144–151
Gago, P., Bento, C.: A Metric for Selection of the Most Promising Rules. Proc. of Euro. Conf. on the Principles of Data Mining and Knowledge Discovery PKDD-1998 (1998) 19–27
Goodman, L. A., Kruskal, W. H.: Measures of association for cross classifications. Springer Series in Statistics, 1, Springer-Verlag (1979)
Gray, B., Orlowska, M. E.: CCAIIA: Clustering Categorical Attributes into Interesting Association Rules. Proc. of Pacific-Asia Conf. on Knowledge Discovery and Data Mining PAKDD-1998 (1998) 132–143
Hamilton, H. J., Shan, N., Ziarko, W.: Machine Learning of Credible Classifications. Proc. of Australian Conf. on Artificial Intelligence AI-1997 (1997) 330–339
Hatazawa, H., Negishi, N., Suyama, A., Tsumoto, S., and Yamaguchi, T.: Knowledge Discovery Support from a Meningoencephalitis Database Using an Automatic Composition Tool for Inductive Applications. Proc. of KDD Challenge 2000 in conjunction with PAKDD2000 (2000) 28–33
Hettich, S., Blake, C. L., and Merz, C. J.: UCI Repository of machine learning databases [http://www.ics.uci.edu/ \tilde{}mlearn/MLRepository.html], Irvine, CA: University of California, Department of Information and Computer Science, (1998)
Hilderman, R. J. and Hamilton, H. J.: Knowledge Discovery and Measure of Interest. Kluwe Academic Publishers (2001)
Hinton, G. E.: “Learning distributed representations of concepts”, Proceedings of 8th Annual Conference of the Cognitive Science Society, Amherest, MA. REprinted in R.G.M. Morris (ed.) (1986)
Holte, R. C.: Very simple classification rules perform well on most commonly used datasets, Machine Learning, Vol. 11 (1993) 63–91
Klösgen, W.: 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. AAAI/MIT Press, California (1996) 249–271
Ohsaki, M., Kitaguchi, S., Kume, S., Yokoi, H., and Yamaguchi, T.: Evaluation of Rule Interestingness Measures with a Clinical Dataset on Hepatitis. Proc. of ECML/PKDD 2004, LNAI3202 (2004) 362–373
Piatetsky-Shapiro, G.: Discovery, Analysis and Presentation of Strong Rules. In Piatetsky-Shapiro, G., Frawley, W. J. (eds.): Knowledge Discovery in Databases. AAAI/MIT Press (1991) 229–248
Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: B. Schölkopf, C. Burges, and A. Smola (eds.): Advances in Kernel Methods - Support Vector Learning, MIT Press (1999) 185–208
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, (1993)
Rijsbergen, C.: Information Retrieval, Chapter 7, Butterworths, London, (1979) http://www.dcs.gla.ac.uk/Keith/Chapter.7/Ch.7.html
Smyth, P., Goodman, R. M.: Rule Induction using Information Theory. In Piatetsky-Shapiro, G., Frawley, W. J. (eds.): Knowledge Discovery in Databases. AAAI/MIT Press (1991) 159–176
Tan, P. N., Kumar V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. Proc. of Int. Conf. on Knowledge Discovery and Data Mining KDD-2002 (2002) 32–41
Witten, I. H and Frank, E.: DataMining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, (2000)
Yao, Y. Y. Zhong, N.: An Analysis of Quantitative Measures Associated with Rules. Proc. of Pacific-Asia Conf. on Knowledge Discovery and Data Mining PAKDD-1999 (1999) 479–488
Zhong, N., Yao, Y. Y., Ohshima, M.: Peculiarity Oriented Multi-Database Mining. IEEE Trans. on Knowledge and Data Engineering, 15, 4, (2003) 952–960
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Abe, H., Tsumoto, S., Ohsaki, M., Yamaguchi, T. (2008). Evaluating Learning Algorithms to Support Human Rule Evaluation with Predicting Interestingness Based on Objective Rule Evaluation Indices. In: Iwata, S., Ohsawa, Y., Tsumoto, S., Zhong, N., Shi, Y., Magnani, L. (eds) Communications and Discoveries from Multidisciplinary Data. Studies in Computational Intelligence, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78733-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-78733-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78732-7
Online ISBN: 978-3-540-78733-4
eBook Packages: EngineeringEngineering (R0)