Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31442
An Intelligent Approach of Rough Set in Knowledge Discovery Databases

Authors: Hrudaya Ku. Tripathy, B. K. Tripathy, Pradip K. Das

Abstract:

Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. In this paper we presented the current status of research on applying rough set theory to KDD, which will be helpful for handle the characteristics of real-world databases. The main aim is to show how rough set and rough set analysis can be effectively used to extract knowledge from large databases.

Keywords: Data mining, Data tables, Knowledge discovery in database (KDD), Rough sets.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327929

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2042

References:


[1] Ryszard S. Michalski and Kenneth A. Kaufman, "Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach", Machine Learning and Data Mining, Methods and Applications, 1997.
[2] J.N.Kok and W.A.Kosters, "Natural Data Mining Techniques", European Association for Theoretical Computer Science, Vol. 71, June 2000, pp.133-142.
[3] Ning ZHONG, Andrzej SKOWRON, "A Rough Set-Based Knowledge Discovery Process", International Journal of Applied Mathematical Computer Science, 2001, Vol.11, No.3, pp.603-619.
[4] Terje L├©ken, "Rough Modeling Extracting Compact Models from Large Databases", Knowledge Systems Group IDI, A thesis submitted to Norwegian University of Science and Technology, 1999.
[5] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "From data mining to knowledge discovery in databases", Artificial Intelligence Magazine 17 (1996), pp.37-54.
[6] Ivo D├╝ntsch, G├╝nther Gediga, Hung Son Nguyen, "Rough set data analysis in the KDD process", published in the Proceedings of IPMU 2000, pp. 220-226.
[7] Hayri Sever, "The Status of Research on Rough Sets for Knowledge Discovery in Databases", www. cuadra.cr.usgs.gov/pubs/srj98.pdf
[8] Deogun.J,Choubey.S,Raghavan.V and Sever.H, "Feature selection and effective classifiers", Journal of ASIS 49, 5 (1998), pp.423-434.
[9] Bell.D, and Guan.J, "Computational methods for rough classification and discovery", Journal of ASIS 49, 5 (1998), pp.403-414.
[10] Deogun.J.S, Raghavan.V.V, and Sever.H, "Exploiting upper approximations in the rough set methodology", In The First International Conference on Knowledge Discovery and Data Mining (Montreal, Quebec, Canada, aug 1995), U. Fayyad and R. Uthurusamy, Eds., pp.69-74.
[11] Kent.R. E, "Rough concept analysis", In Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (Banff, Alberta, Canada, 1993), pp.245-253.
[12] Andrzej Skowron, "Rough Sets in KDD", Institute of Mathematics Warsaw University Banacha 2, 02{095, Warsaw, Poland.