We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Rule Detection in Mobile Ad Hoc Network using Rough Set and Probability

by P. Seethalakshmi, S. Senthilkumar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 11
Year of Publication: 2021
Authors: P. Seethalakshmi, S. Senthilkumar
10.5120/ijca2021920987

P. Seethalakshmi, S. Senthilkumar . Rule Detection in Mobile Ad Hoc Network using Rough Set and Probability. International Journal of Computer Applications. 174, 11 ( Jan 2021), 20-24. DOI=10.5120/ijca2021920987

@article{ 10.5120/ijca2021920987,
author = { P. Seethalakshmi, S. Senthilkumar },
title = { Rule Detection in Mobile Ad Hoc Network using Rough Set and Probability },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2021 },
volume = { 174 },
number = { 11 },
month = { Jan },
year = { 2021 },
issn = { 0975-8887 },
pages = { 20-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number11/31721-2021920987/ },
doi = { 10.5120/ijca2021920987 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:21:48.739455+05:30
%A P. Seethalakshmi
%A S. Senthilkumar
%T Rule Detection in Mobile Ad Hoc Network using Rough Set and Probability
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 11
%P 20-24
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes an innovative approach for rule detection from a decision table. The aim is to apply rough set concepts and probabilistic properties to search for rule discovery. Rough set theory is generally a comparatively new intelligent technique used within the invention of data of knowledge dependencies; it evaluates the importance of attributes, discovers the patterns of information, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. Moreover, it is getting used for the extraction of rules from databases. With every decision rule in decision table, two conditional probabilities, the certainty and the coverage coefficient of the rule are associated. The Probabilistic approach is an extension of the Rough set approach that reveals some probabilistic structure of the data being analyzed. Finally, these techniques will be applied for finding rules in mobile ad hoc network for the selection of best routing path with minimum number of resources.

References
  1. Pawlak, Z. 1982. “Rough sets”. International Journal of Computer and Information Science, 11, 345- 356.
  2. Huanglin, Z. 1996. Rough Set Theory and its Applications. Chongqing University Press, Cbongqing.
  3. Jia-Cheng, W., Hua-Ping, D., and You-Xian, S. 2003. A novel approach to computing rule confidence in rough set theory. Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, 1523-1526.
  4. Jianguo, T. 2002. “Reliability analysis to deal with imperfect information by rough set theory”. Control Decisions, 17(2), 255-256.
  5. Pawlak, Z. 1991. Rough Set-Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht.
  6. Zhang, J., Li, T., and Chen, H. 2014. “Composite rough sets for dynamic data mining”. An International Journal of Information Sciences, 257, 81–100.
  7. Huang, Y., Li, T., Luo, C., Fujita, H., and Horng, S.J. 2017. “Matrix-based dynamic updating rough fuzzy approximations for data mining”.  Knowledge-Based Systems, 119, 273–283.
  8. Gu ShM., Gao, J., and Tian XQ. 2007. “A fuzzy measure based on variable precision rough sets”. In: Cao BY (ed) Fuzzy information and engineering (ICFIE), ASC, 40, 798–807.
  9. Wang L., and Yang, G. 2009. Mining Valid Association Rules in Incomplete Information Systems. First International Workshop on Education Technology and Computer Science, Wuhan, 633-636.
  10. Duntsch, I., Gediga, and Roughian, G. 2001. “Rough information analysis”. International Journal of Intelligent Systems,16,121-147.
  11. Wong, S.K.M., and Ziarko, W. 1987. “Comparison of the probabilistic approximate classification and the fuzzy set model”. Fuzzy Sets and Systems, 21, 357-362.
  12. Greco, S., Matarazzo, B., and Slowinski, R. Rough membership and Bayesian confirmation measures for parameterized rough sets. 2005. LNAI 3641, 314-324.
  13. Greco, S., Pawlak, Z., and Slowinski, R. 2004. “Can Bayesian confirmation measures be useful for rough set decision rules”. Engineering Applications of Artificial Intelligence, 17(4), 345-361.
  14. Slezak, D., and Ziarko, W. 2003. “Attribute reduction in the Bayesian version of variable precision rough set model”. Electronic Notes in Theoretical Computer Science, 82, 263-273.
  15. Slezak, D. 2005. Rough sets and Bayes factor. LNAI 3400, 202-229.
  16. Pawlak, Z., and Skowron, A. 2007. “Rudiments of rough sets”. Information Sciences, 177(1), 3-27.
  17. Zimmermann, H.J. 1991. Fuzzy Set Theory and Its Applications. Kluwer Academic Publishers.
  18. Mehmed Kantardzic, 1993. Data Mining, Concept, Models, Methods, and Algorithms. Wiley Publishers.
  19. Wang, T.C., and Lee, H.D. 2006. “Constructing a Fuzzy Decision Tree by Integrating Fuzzy Sets and Entropy”. WSEAS Transactions on Information Science and Applications, 3(8), 1547–1552.
  20. Pawlak, Z. 2012. Rough set theory and its applications. Journal of Telecommunications and Information Technology.
  21. Hossam A Nabwey. 2011. “A Probabilistic Rough Set Approach to Rule Discovery”. International Journal of Advanced Science and Technology, 30(May, 2011).
  22. NagaRaju, Ramchandran, S. 2008. “Fuzzy Cost Based Multipath Routing for Mobile Ad-Hoc Networks”. Journal of Theoretical and Applied Information Technology, 319-326.
Index Terms

Computer Science
Information Sciences

Keywords

Rough Set Entropy Information gain Certainty and Coverage Decision rule.

Powered by PhDFocusTM