CFP last date
20 December 2024
Reseach Article

Rough set Approach to Find the Cause of Decline of E –Business

by Sujogya Mishra, Shakthi Prasad Mohanty, Sateesh Kumar Pradhan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 144 - Number 12
Year of Publication: 2016
Authors: Sujogya Mishra, Shakthi Prasad Mohanty, Sateesh Kumar Pradhan
10.5120/ijca2016910491

Sujogya Mishra, Shakthi Prasad Mohanty, Sateesh Kumar Pradhan . Rough set Approach to Find the Cause of Decline of E –Business. International Journal of Computer Applications. 144, 12 ( Jun 2016), 12-18. DOI=10.5120/ijca2016910491

@article{ 10.5120/ijca2016910491,
author = { Sujogya Mishra, Shakthi Prasad Mohanty, Sateesh Kumar Pradhan },
title = { Rough set Approach to Find the Cause of Decline of E –Business },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 144 },
number = { 12 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume144/number12/25230-2016910491/ },
doi = { 10.5120/ijca2016910491 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:47:27.237135+05:30
%A Sujogya Mishra
%A Shakthi Prasad Mohanty
%A Sateesh Kumar Pradhan
%T Rough set Approach to Find the Cause of Decline of E –Business
%J International Journal of Computer Applications
%@ 0975-8887
%V 144
%N 12
%P 12-18
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, I am finding the cause of decline of E-Business in our state by using Rough set theory.

References
  1. S.K. Pal, A. Skowron, Rough Fuzzy Hybridization: A new trend in decision making, Berlin, Springer-Verlag, 1999
  2. Z. Pawlak, “Rough sets”, International Journal of Computer and Computer and Information Sciences, Vol. 11, 1982, pp.341–356
  3. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and problem Solving, Vol. 9, The Netherlands, Kluwer - Academic Publishers, Dordrecht, 1991
  4. Han, Jiawei, Kamber, Micheline, Data Mining: Concepts and Techniques. San Franciso CA, USA, Morgan Kaufmann Publishers, 2001
  5. Ramakrishnan, Naren and Grama, Y. Ananth, “Data Mining: From Serendipity to Science”, IEEE Computer, 1999, pp. 34-37.
  6. Williams, J. Graham, Simoff, J. Simeon, DataMining Theory, Methodology, Techniques, andApplications (Lecture Notes in Computer Science/ LectureNotes in Artificial Intelligence), Springer, 2006.
  7. D.J. Hand, H. Mannila, P. Smyth, Principles ofData Mining. Cambridge, MA: MIT Press, 2001
  8. D.J. Hand, G.Blunt, M.G. Kelly, N.M.Adams, “Data mining for fun and profit”, Statistical Science, Vol.15, 2000, pp.111-131.
  9. C. Glymour, D. Madigan, D. Pregibon, P.Smyth, “Statistical inference and data mining”, Communications of the ACM, Vol. 39, No.11,1996, pp.35-41.
  10. T.Hastie, R.Tibshirani, J.H. Friedman, Elements of statistical learning: data mining, inference and prediction, New York: Springer Verlag, 2001
  11. H.Lee, H. Ong, “Visualization support for data Mining”, IEEE Expert, Vol. 11, No. 5, 1996, pp. 69-75.
  12. H. Lu, R. Setiono, H. Liu,“Effective data Mining using neural networks”, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, pp. 957-961.
  13. E.I Altman, “Financial ratios, discriminants analysis and prediction of corporate bankruptcy”, The journal offinance, Vol. 23 , 1968, pp.589-609
  14. E.I.Altman, R.Avery, R.Eisenbeis, J. Stnkey, “Application of classification techniques in business, banking and finance. Contemporary studies in Economicand Financial Analysis”, vol.3, Greenwich, JAI Press,1981.
  15. E.I Altman, “The success of business failureprediction models: An international surveys”, Journal of Banking and Finance Vol. 8, no.2, 1984, pp.171-198
  16. E.I Altman, G. Marco, F. Varetto, “Corporate distressdiagnosis: Comparison using discriminant analysis andneural networks”, Journal of Banking and Finance, Vol. 18, 1994, pp. 505-529
  17. W.H Beaver, “Financial ratios as predictors of failure. Empirical Research in accounting : Selected studies”, Journal of Accounting Research Supplement to Vol4, 1966, pp.71-111
  18. J.K Courtis, “Modelling a financial ratios categoric frame Work”, Journal of Business Finance and Accounting, Vol. 5, No.4, 1978, pp71-111
  19. H.Frydman, E.I Altman ,D-lKao, “Introducing recursivepartitioning for financial classification: the case offinancial distress”, The Journal of Finance, Vol.40, No. 1, 1985, pp. 269-291.
  20. Y.P.Gupta, R.P.Rao, P.K. , Linear Goal programming asan alternative to multivariate discriminant analysis a note journal of business fiancé and accounting Vol.17, No.4, 1990, pp. 593-598
  21. M. Louma, E, K. Laitinen, “Survival analysis as a tool for company failure prediction”. Omega, Vol.19, No.6, 1991, pp. 673-678
  22. W.F. Messier, J.V. Hanseen, “Including rules for expert system development: an example using default and bankruptcy data”, Management Science, Vol. 34, No.12, 1988, pp.1403-1415
  23. E.M. Vermeulen, J. Spronk, N. Van der Wijst., The application of Multifactor Model in the analysis of corporate failure. In: Zopounidis,C.(Ed), Operationalcorporate Tools in the Management of financial Risks, Kluwer Academic Publishers, Dordrecht, 1998, pp. 59-73
  24. C. Zopounidis, A.I. Dimitras, L. Le Rudulier, A multicriteria approach for the analysis and prediction of business failure in Greece. Cahier du LAMSADE, No. 132, Universite de Paris Dauphine, 1995.
  25. C. Zopounidis, N.F. Matsatsinis, M. Doumpos, “Developing a multicriteria knowledge-based decision support system for the assessment of corporateperformance and viability: The FINEVA system, “Fuzzy Economic Review, Vol. 1, No. 2, 1996, pp. 35-53.
  26. C. Zopounidis, M. Doumpos, N.F. Matsatsinis, “Application of the FINEVA multicriteria knowledge-decision support systems to the assessment of corporatefailure risk”, Foundations of Computing and Decision Sciences, Vol. 21, No. 4, 1996, pp. 233-251
  27. RenuVashist Prof M.L Garg Rule Generation based on Reduct and Core :A rough set approach InternationalJournal of Computer Application(0975-887) Vol29
Index Terms

Computer Science
Information Sciences

Keywords

Set Theory Data Analysis Granular computing Data mining