Research Article

Why there is so much difference between Contractual and Regular Employees in a Government Run Organization by using Rough Set

by  Sujogya Mishra, Radhanath Hota, Anshuman Mishra
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Issue 12
Published: February 2016
Authors: Sujogya Mishra, Radhanath Hota, Anshuman Mishra
10.5120/ijca2016908218
PDF

Sujogya Mishra, Radhanath Hota, Anshuman Mishra . Why there is so much difference between Contractual and Regular Employees in a Government Run Organization by using Rough Set. International Journal of Computer Applications. 136, 12 (February 2016), 5-11. DOI=10.5120/ijca2016908218

                        @article{ 10.5120/ijca2016908218,
                        author  = { Sujogya Mishra,Radhanath Hota,Anshuman Mishra },
                        title   = { Why there is so much difference between Contractual and Regular Employees in a Government Run Organization by using Rough Set },
                        journal = { International Journal of Computer Applications },
                        year    = { 2016 },
                        volume  = { 136 },
                        number  = { 12 },
                        pages   = { 5-11 },
                        doi     = { 10.5120/ijca2016908218 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2016
                        %A Sujogya Mishra
                        %A Radhanath Hota
                        %A Anshuman Mishra
                        %T Why there is so much difference between Contractual and Regular Employees in a Government Run Organization by using Rough Set%T 
                        %J International Journal of Computer Applications
                        %V 136
                        %N 12
                        %P 5-11
                        %R 10.5120/ijca2016908218
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

In our country many college and school run by government agencies. There are two categories of employment, one is regular and the other one is contractual. The idea of this paper is conceived looking at violation of human rights in these places. Regular employees enjoy all the facilities such as library, job security air condition chambers in contrast contractual employees deprived from all these facilities. Our intention to find the parameter for why the above said happened by the use of rough set theory ..

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, 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 failureprediction 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 distressdiagnosis: 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 frameWork”, 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 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.
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Rough Set Theory data analysis Granular computing Data mining

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