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Reseach Article

Rough Set Approach in Finding the Cause of Decline and Down Fall of Jute Industries and the Remedy

by Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 19
Year of Publication: 2015
Authors: Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota

Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota . Rough Set Approach in Finding the Cause of Decline and Down Fall of Jute Industries and the Remedy. International Journal of Computer Applications. 121, 19 ( July 2015), 35-41. DOI=10.5120/21650-4900

@article{ 10.5120/21650-4900,
author = { Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota },
title = { Rough Set Approach in Finding the Cause of Decline and Down Fall of Jute Industries and the Remedy },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 19 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { },
doi = { 10.5120/21650-4900 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:08:52.067440+05:30
%A Sujogya Mishra
%A Shakti Prasad Mohanty
%A Sateesh Kumar Pradhan
%A Radhanath Hota
%T Rough Set Approach in Finding the Cause of Decline and Down Fall of Jute Industries and the Remedy
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 19
%P 35-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The jute industries 10 to 15 years back especially in south Asian countries are bread and butter for the people belonging to middle and lower income group , now the scenario is completely different. In the present scenario jute is found to be expensive and not much useful as compared to other parallel packaging material available in the market due for this reason most of the jute mills suffered, from severe financial crisis which forced the jute mills owner to close down their unit . In this context we tries to find the cause of failure of jute industries in recent age and how to develop the jute industries in recent age,. For this purpose we develop an algorithm by using rough set concept on data which we gathered from different sources, develop algorithm is simple and user friendly then validate this concept by using statistical validation method in our paper we basically focused on issues which leads to sick jute industries. Initially we gathered 10000 samples for our purpose then applying correlation technique on the collected data the data set reduced to 20 which are dissimilar in nature . Once we have the data set by correlation technique we then apply rough set techniques on those data to generate an efficient algorithm . The entire paper is sub divided into three sections. Section 1 deal with literature review and last two section deals with the experimental result and statistical validation of our proposed algorithm.

  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. 11 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
Index Terms

Computer Science
Information Sciences


Rough Set Theory Raw data regarding Jute industries Granular computing Data mining.