Abstract
Ordered information is a kind of useful background knowledge to guide a discovery process toward finding different types of novel rules and improving their quality for many real world data mining tasks. In the paper, we investigate ways of using ordered information for gastric cancer data mining, based on rough set theory and granular computing. With respect to the notion of ordered information tables, we describe how to mine ordering rules and how to form granules of values of attributes in a pre/post-processing step for improving the quality of the mined classification rules. Experimental results in gastric cancer data mining show the usefulness and effectiveness of our approaches.
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
Cohen W.W., Schapire R.E., and Singer Y. “Learning to Order Things”, Advances in Neural Information Processing Systems, Vol 10 (1998).
Dougherty, J, Kohavi, R., and Sahami, M. “Supervised and Unsupervised Discretization of Continuous Features”, Proc. 12th Inter. Conf. on Machine Learning (1995) 194–202.
Greco S., Matarazzo B., and Slowinski R. “Rough Approximation of a Preference Relation by Dominance Relations”, European Journal of Operational Research Vol. 117 (1999) 63–83.
Iwinski T.B. “Ordinal Information System”, Bulletin of the Polish Academy of Sciences, Mathematics, Vol. 36 (1998) 467–475.
Lin, T.Y. and Cercone, N. (ed.) Rough Sets and Data Mining: Analysis of Imprecise Data, Kluwer (1997).
Nguyen, H. Son, Skowron, A. “Boolean Reasoning for Feature Extraction Problems”, Z.W. Ras, A. Skowron (eds.), Foundations of Intelligent Systems, LNAI 1325, Springer (1997) 117–126.
Nguyen H. Son and Nguyen S. Hoa “Discretization Methods in Data Mining”, L. Polkowski, A. Skowron (eds.) Rough Sets in Knowledge Discovery, Physica-Verlag (1998) 451–482.
Pawlak, Z. Rough Sets, Theoretical Aspects of Reasoning about Data, Kluwer (1991).
Pawlak Z. and Slowinski R. “Rough set approach to multi-attribute decision analysis”. European Journal of Operational Research, Vol. 72 (1994) 443–359.
Sai, Y., Yao, Y.Y., and Zhong, N. “Data Analysis and Mining in Ordered Information Tables”, Proc. 2001 IEEE International Conference on Data Mining (IEEE ICDM’01), IEEE Computer Society Press (2001) 497–504.
Yao, Y.Y. and Zhong, N. “Potential Applications of Granular Computing in Knowledge Discovery and Data Mining”, Proc. 5th Inter. Conf. on Information Systems Analysis and Synthesis (IASA’99) (1999) 573–580.
Yao, Y.Y. “Information Tables with Neighborhood Semantics”, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, Dasarathy, B.V. (Ed.), Society for Optical Engineering, Bellingham, Washington (2000) 108–116.
Yao, Y.Y. and Sai, Y. “Mining Ordering Rules Using Rough Set Theory”, Bulletin of International Rough Set Society, Vol. 5 (2001) 99–106.
Zadeh, L.A. “Fuzzy Sets and Information Granularity”, Gupta, N., Ragade, R., and Yager, R. (Eds.) Advances in Fuzzy Set Theory and Applications, North-Holland (1979) 3–18.
Zadeh, L. A. “Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic”, Fuzzy Sets and Systems, Elsevier, Vol 90 (1997) 111–127.
Zhong, N., Dong, J.Z., and Ohsuga, S. “Using Background Knowledge as a Bias to Control the Rule Discovery Process”, Djamel A. Zighed, Jan Komorowski, and J. Zytkow (eds.) Principles of Data Mining and Knowledge Discovery. LNAI 1910, Springer (2000) 691–698.
Zhong, N., Dong, J.Z., and Ohsuga, S. “Rule Discovery by Soft Induction Techniques”, Neurocomputing, An International Journal, Vol. 36(1-4) Elsevier (2001) 171–204.
Zhong, N. and Skowron, A. “A Rough Sets Based Knowledge Discovery Process”, International Journal of Applied Mathematics and Computer Science, Vol. 11, No. 3, Technical University Press, Poland (2001) 101–117.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhong, N., Dong, JZ., Yao, Y.Y., Ohsuga, S. (2002). Gastric Cancer Data Mining with Ordered Information. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_62
Download citation
DOI: https://doi.org/10.1007/3-540-45813-1_62
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44274-5
Online ISBN: 978-3-540-45813-5
eBook Packages: Springer Book Archive