Research Article
Parameters to find the cause of Global Terrorism using Rough Set Theory
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 127 - Issue 14 |
| Published: October 2015 |
| Authors: Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota |
10.5120/ijca2015906635
|
Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota . Parameters to find the cause of Global Terrorism using Rough Set Theory. International Journal of Computer Applications. 127, 14 (October 2015), 46-50. DOI=10.5120/ijca2015906635
@article{ 10.5120/ijca2015906635,
author = { Sujogya Mishra,Shakti Prasad Mohanty,Sateesh Kumar Pradhan,Radhanath Hota },
title = { Parameters to find the cause of Global Terrorism using Rough Set Theory },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 127 },
number = { 14 },
pages = { 46-50 },
doi = { 10.5120/ijca2015906635 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Sujogya Mishra
%A Shakti Prasad Mohanty
%A Sateesh Kumar Pradhan
%A Radhanath Hota
%T Parameters to find the cause of Global Terrorism using Rough Set Theory%T
%J International Journal of Computer Applications
%V 127
%N 14
%P 46-50
%R 10.5120/ijca2015906635
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Terrorism is curse to mankind ,it affect globally as an unit, in this paper our intention to find the cause why young people attracted towards terrorism ,to find this we are using rough set concept which provide us the approximate parameter for terrorism
References
- Cios, K., W. Pedrycz and R. Swiniarski (1998). Data Mining Methods for Knowledge Discovery. Kluwer Academic
- Wolf, S., H. Oliver, S. Herbert and M. Michael (2000). Intelligent data mining for medical quality management
- Se-Ho, Ch., and P. Rockett (2002). The training of neural classifiers with condensed datasets. SMCB, 32(2), 202–206.,
- Setiono, R. (2000). Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine, 18(3), 205–219
- Cheeseman, P., and J. Stutz (1996). Bayesian classification (AutoClass): theory and results. In U.M.Fayyad Grzymala–Busse, J., Z. Pawlak, R. Slowinski and W. Ziarko (1999). Rough sets. Communications of the ACM
- Hassanien, A.E. (2003). Classification and feature selection of breast cancer data based on decision tree algorithm
- Parido, A., and P. Bonelli (1993). A new approach to fuzzy classifier systems. In Proceedings of the FifthInternational Conference on Genetic Algorithms. pp. 223–230
- Lin, T.Y., and N. Cercone (1997). Rough Sets and Data Mining. Kluwer Academic Publishers.Ning, S., H. Xiaohua, W. Ziarko and N. Cercone (1994). A generalized rough sets model. In Proceedings ofthe 3rd Pacific Rim International Conference on Artificial Intelligence, Vol. 431. Beijing, China. Int. Acad.Publishers. pp. 437–443.
- Pawlak, Z. (1991). Rough Sets-Theoretical Aspect of Reasoning about Data. Kluwer Academic Publishers.
- Pawlak, Z., J. Grzymala–Busse, R. Slowinski, W. Ziarko (1995). Rough sets. Communications of the ACM
- Renu Vashist Prof M.L Garg Rule Generation based on Reduct and Core :A rough set approach International Journal of Computer Application(0975-887) Vol 29 September -2011 Page 1-4.
Index Terms
Keywords