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Rough Set Approach for the Classification of Advertisement in the Development of Business Establishment

by Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 5
Year of Publication: 2015
Authors: Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan
10.5120/19315-0778

Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan . Rough Set Approach for the Classification of Advertisement in the Development of Business Establishment. International Journal of Computer Applications. 110, 5 ( January 2015), 34-41. DOI=10.5120/19315-0778

@article{ 10.5120/19315-0778,
author = { Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan },
title = { Rough Set Approach for the Classification of Advertisement in the Development of Business Establishment },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 5 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number5/19315-0778/ },
doi = { 10.5120/19315-0778 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:45:34.954989+05:30
%A Sujogya Mishra
%A Shakti Prasad Mohanty
%A Sateesh Kumar Pradhan
%T Rough Set Approach for the Classification of Advertisement in the Development of Business Establishment
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 5
%P 34-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the current age business establishments are basically depends upon advertisement to attain success . In this paper we consider different forms of advertisements then using rough set concept, we find the best possible forms of advertisement. To develop this concept we consider 1000 samples initially and applying correlation techniques the number reduces to 20 which appears to be dissimilar with respect to advertisements initially. We classified the entire paper in to four section , section 1 deals with the literature review and in the section 2 deals with the experiment on the data which we collected from different sources and in last two section deals with the algorithm which we develop using rough set concept and validation of the algorithm using statistical test

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, Data Mining Theory, Methodology, Techniques, and Applications (Lecture Notes in Computer Science/ Lecture Notes in Artificial Intelligence), Springer, 2006.
  7. D. J. Hand, H. Mannila, P. Smyth, Principles of Data 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 of finance, 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 Economic and Financial Analysis", vol. 3, Greenwich, JAI Press,1981.
  15. E. I Altman, "The success of business failure prediction 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 distress diagnosis: Comparison using discriminant analysis and neural 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 Vol 4, 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 recursive partitioning for financial classification: the case of financial 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 as an 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), Operational corporate 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 corporate performance 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 corporate failure risk", Foundations of Computing and Decision Sciences, Vol. 21, No. 4, 1996, pp. 233-251
  27. Renu Vashist Prof M. L Garg Rule Generation based on Reduct and Core :A rough set approach InternationalJournal of Computer Application(0975-887) Vol 29 September -2011 Page 1-4
Index Terms

Computer Science
Information Sciences

Keywords

Rough Set Theory business data Granular computing Data mining

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