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Rule Generation based on Reduct and Core: A Rough Set Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 29 - Number 9 |
| Year of Publication: 2011 |
| Authors: Renu Vashist, Prof. M.L Garg |
10.5120/3595-4989
|
Renu Vashist, Prof. M.L Garg . Rule Generation based on Reduct and Core: A Rough Set Approach. International Journal of Computer Applications. 29, 9 ( September 2011), 1-5. DOI=10.5120/3595-4989
Abstract
Rough set theory has evolved as one of the most important technique used for feature selection as a result of contemporary developments in data mining. One of the cardinal uses of Rough set theory is its application for rule generation. More often attribute reduction poses a major challenge for developing the theory and applications of rough set. This paper proposes a unique mathematical approach for determining the most important attribute with the help of confidence and strength of an association. Our approach focuses on the elimination of the redundant attributes in order to generate the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Subsequently, only a subset of feature is selected which retain the accuracy of the original features.
References
- Pal S.K., Skowron (Eds.), A. 1999. Rough Fuzzy Hybridization: A new trend in decision making. Springer-Verlag, Berlin.
- Pawlak, Z. 1982. Rough sets, International Journal of Computer and Information Sciences 11: 341–356.
- Pawlak, Z. 1991. Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9, Kluwer Academic Publishers, Dordrecht, The Netherlands.
- Han, Jiawei., Kamber, Micheline. 2001. Data Mining: Concepts and Techniques. San Franciso CA, USA, Morgan Kaufmann Publishers.
- Ramakrishnan., Naren and Grama, Ananth Y. 1999. Data Mining: From Serendipity to Science. IEEE Computer, August 1999: 34-37.
- Williams, Graham J. and Simoff, Simeon J. 2006. Data Mining Theory, Methodology, Techniques, and Applications (Lecture Notes in Computer Science/ Lecture Notes in Artificial Intelligence), Springer.
- Hand, D.J., Mannila, H., & Smyth, P. 2001. Principles of Data Mining. Cambridge, MA: MIT Press.
- Hand, D.J., Blunt, G., Kelly, M.G. & Adams, N.M. 2000. Data mining for fun and profit. Statistical Science, 15, 111-131.
- Glymour, C., Madigan, D., Pregibon, D., and Smyth, P. 1996. Statistical inference and data mining. Communications of the ACM, 39(11):35-41.
- Hastie, T., Tibshirani, R., & Friedman, J.H. 2001. Elements of statistical learning: data mining, inference and prediction. New York: Springer Verlag.
- Lee, H. and Ong, H., 1996. Visualization support for data mining. IEEE Expert, 11(5):69-75.
- Lu, H., Setiono, R., and Liu, H. 1996. Effective data mining using neural networks. IEEE Transactions on Knowledge and Data Engineering, 8(6):957-961.
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