Abstract
The main difference between conventional data analysis and KDD (Knowledge Discovery and Data mining) is that the latter approaches support discovery of knowledge in databases whereas the former ones focus on extraction of accurate knowledge from databases. Therefore, for application of KDD methods, domain experts’ interpretation of induced results is crucial. However, conventional approaches do not focus on this issue clearly. In this paper, 11 KDD methods are compared by using a common medical database and the induced results are interpreted by a medical expert, which enables us to characterize KDD methods more concretely and to show the importance of interaction between KDD researchers and domain experts.
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
Adams, R., Victor, M.: Principles of Neurology, 5th edn. McGraw-Hill, New York (1993)
Akaike, H.: Information theory and an extention of the maximum likelihood principle. In: Petrov, B., Csaki, F. (eds.) 2nd International Symposium on Information Theory, pp. 267–281. Akadimiai Kiado, Budapes (1973)
Durand, M.L., Calderwood, S.B., Weber, D.J., Miller, S.I., Southwick, F.S., Verne, S., Caviness, J., Swartz, M.N.: Acute bacterial meningitis in adults - a review of 493 episodes. New England Journal of Medicine 328, 21–28 (1993)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. CACM 29, 27–34 (1996)
Logan, S.A.E., MacMahon, E.: Viral meningitis. British Medical Journal 336, 36 (2008)
Rissanen, J.: Stochastic Complexity in Statistical Inquiry. World Scientific, Singapore (1989)
Tsumoto, S., et al.: Examination of factors important to predict the prognosis of virus meningoencephalitis. Japanese Kanto Journal of Acute Medicine 12, 710–711 (1991)
Shapiro, G., Frawley, W. (eds.): Knowledge Discovery in Databases. AAAI Press, Palo alto (1991)
Shavlik, J., Dietterich, T. (eds.): Readings in Machine Learning. Morgan Kaufmann, Palo Alto (1990)
Suzuki, E.: Exceptional rule discovery in databases based on information theory. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 275–278. AAAI Press, Menlo Park (1996)
Terano, T., Ishino, Y.Y.: Interactive genetic algorithm based feature seelction and its application to marketing data analysis. In: Liu, H., Motada, H. (eds.) Feature Extraction Concstruction and Selection: A Data Mining Perspective, pp. 393–406. Kluwer, Dordrecht (1998)
Tsukada, M., Inokuchi, A., Washio, T., Motoda, H.: Comparison of mdlp and aic on discretization of numerical attributes. In: Proceedings of 42nd KBS Meeting: SIG-KBS-9802, pp. 45–52. Japan AI Society (1999) (in Japanese)
Tsumoto, S., Ziarko, W.N.S., Tanaka, H.: Knowledge discovery in clinical databases based on variable precision rough set model. In: The Eighteenth Annual Symposium on Computer Applications in Medical Care, pp. 270–274 (1995)
Tsumoto, S., Yamaguti, T. (eds.): Proceeding of 42nd KBS meeting: SIG-KBS-9802. Japanese AI Society (1999)
Zhong, N., Dong, J., Ohsuga, S.: Data mining based on the generalization distribution table and rough sets. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 360–373. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsumoto, S., Takabayashi, K. (2011). Data Mining in Meningoencephalitis: The Starting Point of Discovery Challenge. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-21916-0_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21915-3
Online ISBN: 978-3-642-21916-0
eBook Packages: Computer ScienceComputer Science (R0)