Abstract

The idea of the paper conceived looking at present accident rate, this is mainly because of faulty traffic rules. We develop a rule based upon rough set theory, which provide a suggestion to the agencies responsible for traffic control.

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, “DataMining: From Serendipity to Science”, IEEE Computer, 1999, pp. 34-37.
• Williams, J. Graham, Simoff, J. Simeon, DataMining Theory, Methodology, Techniques, andApplications (Lecture Notes in Computer Science/ LectureNotes in Artificial Intelligence), Springer, 2006.
• D.J. Hand, H. Mannila, P. Smyth, Principles ofData 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,.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 dataMining”, 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
• 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.