Journal of Engineering and Applied Sciences

Year: 2017
Volume: 12
Issue: 22
Page No. 6069 - 6075

Adaptive Model for Campus Placement Prediction using Improved Decision Tree

Authors : Subitha Sivakumar and Rajalakshmi Selvaraj

References

Al-Radaideh, Q.A., E.M. Al-Shawakfa and M.I. Al-Najjar, 2006. Mining student data using decision trees. Proceedings of the 2006 International Arab Conference on Information Technology (ACIT'06), December 19-21, 2006, Yarmouk University, Irbid, Jordan, pp: 1-5.

Ayesha, S., T. Mustafa, A. Sattar and I. Khan, 2010. Data mining model for higher education system. Eur. J. Sci. Res., 43: 24-29.
Direct Link  |  

Baker, R.S.J.D., 2010. Data Mining for Education. In: International Encyclopedia of Education, McGaw, B., P. Peterson and E. Baker (Eds.). Elsevier, Oxford, UK., pp: 112-118.

Ben-Zadok, G., A. Hershkovitz, R. Mintz and R. Nachmias, 2007. Examining online learning processes based on log files analysis: A case study. Res. Reflection Innovations Integrating ICT Educ., 2007: 55-59.

Bharadwaj, B.K. and S. Pal, 2011. Data mining: A prediction for performance improvement using classification. Int. J. Comput. Sci. Info., Security (IJCSIS), 9: 136-140.
Direct Link  |  

Bharadwaj, B.K. and S. Pal, 2011. Mining educational data to analyze student's performance. Int. J. Adv. Comput. Sci. Applic., 2: 63-69.
Direct Link  |  

Bray, M., 2007. The Shadow Education System: Private Tutoring and its Implications for Planners. 2nd Edn., UNESCO, Paris, France, ISBN:9789280313055, Pages: 101.

Bresfelean, V.P., 2007. Analysis and predictions on students behavior using decision trees in Weka environment. Proceedings of the 29th International Conference on Information Technology Interfaces (ITI’07), June 25-28, 2007, IEEE, Cavtat, Croatia, ISBN:953-7138-09-7, pp: 25-28.

Chang, T. and D. Ed, 2008. Data mining: A magic technology for college recruitment. Master Thesis, Overseas Chinese Association for Institutional Research, Santa Ana, California.

Cortez, P. and A.M.G. Silva, 2008. Using data mining to predict secondary school student performance. Proceedings of the 5th Annual Conference on Future Business Technology (FUBUTEC’08), April 9-11, 2008, EUROSIS, Porto, Portugal, ISBN:978-9077381-39-7, pp: 5-12.

Elayidom, S., S.M. Idikkula and J. Alexander, 2011. A generalized data mining framework for placement chance prediction problems. Intl. J. Comput. Appl., 31: 40-47.
Direct Link  |  

Han, J. and M. Kamber, 2000. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Burlington, Massachusetts, USA.,.

Hijazi, S. and S. Naqvi, 2006. Factors affecting students performnce: A case of Private Colleges, Bangladesh. J. Sociology, 3: 12-17.
Direct Link  |  

Kabra, R.R. and R.S. Bichkar, 2011. Performance prediction of engineering students using decision trees. Intl. J. Comput. Appl., 36: 8-12.
Direct Link  |  

Khan, Z.N., 2005. Scholastic achievement of higher secondary students in science stream. J. Soc. Sci., 1: 84-87.
CrossRef  |  Direct Link  |  

Kovacic, Z., 2010. Early prediction of student success: Mining students enrolment data. Proceedings of the 2010 Conference on Informing Science and IT Education (InSITE), June 19 - 24 2010, Information Sciences Institute, Cassino, Italy, pp: 1-17.

Moucary, C.E., 2011. Data mining for engineering schools predicting students performance and enrollment in masters programs. Intl. J. Adv. Comput. Sci. Appl., 2: 1-9.
Direct Link  |  

Pal, A.K. and S. Pal, 2013. Analysis and mining of educational data for predicting the performance of students. Intl. J. Electron. Commun. Comput. Eng., 4: 1560-1565.

Pandey, U.K. and S. Pal, 2011. A data mining view on class room teaching language. (IJCSI) Int. J. Comput. Sci., 8: 277-282.

Quinlan, J.R., 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA., USA.

Ramesh, V., P. Parkavi and P. Yasodha, 2011. Performance analysis of data mining techniques for placement chance prediction. Intl. J. Sci. Eng. Res., 2: 1-7.

Witten, I.H. and E. Frank, 2000. Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edn., Morgan Kaufmann Publishers, San Francisco, USA., ISBN:1-55860-552-5, Pages: 373.

Wu, X. and V. Kumar, 2009. The Top Ten Algorithms in Data Mining. CRC Press, Boca Raton, Florida, USA., ISBN-13:978-1-4200-8964-6, Pages: 214.

Yadav, S.K., B. Bharadwaj and S. Pal, 2012. Mining education data to predict student’s retention: A comparative study. Intl. J. Comput. Sci. Inf. Secur., 10: 113-117.
Direct Link  |  

Yadav, S.K., B.K. Bharadwaj and S. Pal, 2012. Data mining applications: A comparative study for predicting student's performance. Int. J. Innovative Technol. Creative Eng., (IJITCE), 1: 13-19.
Direct Link  |  

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