Abstract
The existing rough set based methods are not applicable for large data set because of the high time and space complexity and the lack of scalability. We present a classification method, which is equivalent to rough set based classification methods, but is scalable and applicable for large data sets. The proposed method is based on lazy learning idea[2] and Apriori algorithm for sequent item-set approaches [1]. In this method the set of decision rules matching the new object is generated directly from training set. Accept classification task, this method can be used for adaptive rule generation system where data is growing up in time.
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
Agrawal R., Mannila H., Srikant R., Toivonen H., Verkamo A.I.: Fast discovery of assocation rules. In V.M. Fayad, G. Piatetsky Shapiro, P. Smyth, R. Uthurusamy (eds): Advanced in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996, pp. 307–328.
Aha D.W. (Editorial): “Special Issue on Lazy Learning”, Artificial Intelligence Review, 11(1–5), 1997, pp. 1–6.
Bazan J.: A comparison of dynamic non-dynamic rough set methods for extracting laws from decision tables. In: L. Polkowski and A. Skowron (Eds.), Rough Sets in Knowledge Discovery 1, Physica-Verlag, Heidelberg, 1998, pp. 321–365.
Komorowski J., Pawlak Z., Polkowski L. and Skowron A.: Rough sets: A tutorial. In: S.K. Pal and A. Skowron (eds.), Rough-fuzzy hybridization: A new trend in decision making, Springer-Verlag, Singapore, 1999, pp. 3–98.
Manish Mehta, Rakesh Agrawal, and Jorma Rissanen. SLIQ: A fast scalable classifier for data mining. In Proc. of the Fifth Int’l Conference on Extending Database Technology (EDBT), Avignon, France, March 1996. pp. 18–32
Ohrn A., Komorowski J., Skowron A., Synak P.: The ROSETTA Software System. In Polkowski, L., Skowron, A. (Eds.): Rough Sets in Knowledge Discovery Vol. 1, 2, Springer Physica-Verlag, Heidelberg, 1998, pp. 572–576.
Pawlak Z.: Rough sets: Theoretical aspects of reasoning about data, Kluwer Dordrecht, 1991.
J. Shafer, R. Agrawal, and M. Mehta. SPRINT: A scalable parallel classifier for data mining. In Proc. 1996 Int. Conf. Very Large Data Bases, Bombay, India, Sept. 1996, pp. 544–555.
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In. R. Solowiński (ed.). Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht, 1992, pp. 311–362
Stefanowski J.: On rough set based approaches to induction of decision rules. In: A. Skowron, L. Polkowski (red.), Rough Sets in Knowledge Discovery Vol 1, Physica Verlag, Heidelberg, 1998, 500–529.
Wróblewski J., 1998. Covering with reducts-a fast algorithm for rule generation. In L. Polkowski and A. Skowron (Eds.): Proc. of RSCTC’98, Warsaw, Poland. Springer-Verlag, Berlin Heidelberg, pp. 402–407.
Ziarko, W.: Rough set as a methodology in Data Mining. In Polkowski, L., Skowron, A. (Eds.): Rough Sets in Knowledge Discovery Vol. 1, 2, Springer Physica-Verlag, Heidelberg, 1998, pp. 554–576.
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
Nguyen, H.S. (2002). Scalable Classification Method Based on Rough Sets. 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_57
Download citation
DOI: https://doi.org/10.1007/3-540-45813-1_57
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