Abstract

The college industry in particular private engineering college fail to survive because of proper planning . Once the student strength decreases, the proprietor of the college usually face huge financial loss. To avoid such loss and to sustain in the market we proposed an algorithm which is simple and it is based on rough set theory and then validate this concept by using statistical validation method. Initially we start with 100 samples then by using correlation technique we find 20 dissimilar samples, we then apply rough set theory on those data to develop an algorithm. The entire paper sub divided in to three sections. Section 1 deal with literature review and last two section deals with the experimental result and validation of our proposed algorithm.

References

• S. K. Pal, A. Skowron, Rough Fuzzy Hybridization: A new trend in decision making, Berlin, Springer-Verlag, 1999
• Z. Pawlak, "Rough sets", International Journal of Computer and Computer and Information Sciences, Vol. 11, 1982, pp. 341–356
• 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
• Han, Jiawei, Kamber, Micheline, Data Mining:Concepts and Techniques. San Franciso CA, USA, Morgan Kaufmann Publishers, 2001
• Ramakrishnan, Naren and Grama, Y. Ananth, "Data Mining: From Serendipity to Science", IEEE Computer, 1999, pp. 34-37.
• 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.
• D. J. Hand, H. Mannila, P. Smyth, Principles of Data Mining. Cambridge, MA: MIT Press, 2001
• 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.
• 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.
• T. Hastie, R. Tibshirani, J. H. Friedman, Elements of statistical learning: data mining, inference and prediction, New York: Springer Verlag, 2001
• H. Lee, H. Ong, "Visualization support for data Mining", IEEE Expert, Vol. 11, No. 5, 1996, pp. 69-75.
• 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.
• E. I Altman, "Financial ratios, discriminants analysis and prediction of corporate bankruptcy", The journal of finance, Vol. 23 , 1968, pp. 589-609
• 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.
• 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
• 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
• 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
• J. K Courtis, "Modelling a financial ratios categoric frame Work", Journal of Business Finance and Accounting, Vol. 5, No. 4, 1978, pp71-111
• 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.
• 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
• M. Louma, E, K. Laitinen, "Survival analysis as a tool for company failure prediction". Omega, Vol. 19, No. 6, 1991, pp. 673-678
• 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
• 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
• 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.
• 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.
• 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
• 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