Abstract
In this paper we propose a new method for ranking decision rules generated from an information system. This process will reduce the overhead incurred in selecting appropriate rules for classification and hence speed up the decision making process. The algorithm proposed for rule ranking is based on discernibility matrix in Rough Set Theory. In this approach, rules generated from the given dataset using Apriori algorithm are considered as conditional attributes to construct a new decision table. From this decision table, degree of significance of each rule is calculated and rules are ranked according to this degree of significance. The algorithm is explained with the help of a test dataset. Further it is applied on a Learning Disability (LD) dataset consisting of signs and symptoms causing learning disability, which is collected from a local clinic handling learning disability in school aged children. The experiments on these datasets show that the new method is efficient and effective for ranking decision rules.
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.
Similar content being viewed by others
References
Pawlak, Z.: Rough sets and intelligent data analysis. Information Sciences 147, 1–12 (2002)
Jensen, R., Shen, Q.: New approaches to Fuzzy–Rough Feature Selection. IEEE Transactions on Fuzzy Systems 17(4) (August 2009)
Li, J., Cercone, N.: Discovering and Ranking Important Rules. In: KDM Workshop, Waterloo, Canada, October 30-31 (2006)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Elsevier (2006)
Li, J.: Rough Set Based Rule Evaluations and their Applications. Ph.D thesis from Internet (2007)
Pawlak, Z.: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Maimon, O., Rokach, L.: The Data Mining and Knowledge Discovery Handbook. Springer, Heidelberg (2005)
Tan, S., Wang, Y., Cheng, X.: An Efficient Feature Ranking Measure text Categorization. In: Proceedings of the ACM symposium on Applied Computing, New York (2008)
Grzymala-Busse, J.W.: Rough Set Theory with Applications to Data Mining from Internet
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp. 487–499. Morgan Kaufmann, San Francisco (1994)
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
Sabu, M.K., Raju, G. (2011). A Rough Set Based Approach for Ranking Decision Rules. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22709-7_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-22709-7_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22708-0
Online ISBN: 978-3-642-22709-7
eBook Packages: Computer ScienceComputer Science (R0)