K-mean and EM Clustering algorithm using attendance performance improvement Primary school Student

Authors(3) :-Manmohan Singh, Harish Nagar, Anjali Sant

Based on the clustering methods such as centroid based, distribution based and density based clustering. Cluster includes groups in with small distance among the cluster members. The performance of studentís multilevel of optimization formulated by using clustering. In centroid based clustering, clusters are represented by a central vector. The number of clusters is fixed to k, k-means clustering gives a formal definition as an optimization problem. The clustering model most closely related to statistics is based on distribution model. Experiments attempts to improve the accuracy by using the method of data mining using weak Tool.

Authors and Affiliations

Manmohan Singh
Department of Computer Science and Engineering,. Mewar University, Rajasthan, India
Harish Nagar
Department of Computer Science and Engineering,. Mewar University, Rajasthan, India
Anjali Sant
Department of Computer Science and Engineering,. Mewar University, Rajasthan, India

Centroid based, Density based Cluster, K-means algorithm, EM Clustring Algorithem

  1. Anand Kumar N V, Uma G V 2009, Improving Academic Performance of Students by Applying Data Mining Technique, European Journal of Scientific Research ISSN 1450-216X Vol.34 No.4 , pp.526-534.
  2. Ayesha S, Mustafa T, Sattar A.R., and.Khan, M.I 2010 Data Mining Model for Higher Education System, European Journal of ScientificResearch, Vol.43, No.1, pp.24-29
  3. Brijesh Kumar Baradwaj & SaurabhPal, 2011”Mining Educational Data to analyses Student’s performance”, (IJACSA) International Journalof Advanced Computer Science and Applications, Vol. 2, No. 6, 2011, p 63-69.
  4. Bray M 2007, the Shadow Education System: Private Tutoring And Its Implications For Planners, (2nd ed.), UNESCO, Paris, France.
  5. Banerjee, Abhijit, Rukmini Banerji, Esther Duflo, and MichaelWalton. 2012. Effective Pedagogies and a Resistant Education System:Experimental Evidence on Interventions to Improve Basic Skills in Rural India. MIT
  6. Chandra, E. and Nandhini, K. 2010 .Knowledge Mining from Student Data‘, European Journal of Scientific Research, vol. 47, no. 1, pp. 156-163.
  7. Carlos Márquez-Vera, Cristóbal Romero Morales, and Sebastián Ventura Soto. 2013, 'Predicting School Failure and Dropout by Using Data Mining Techniques' , IEEE Journal Of Latin-American Learning Technologies, Vol. 8, No. 1, Feb 2013 .
  8. Dréze, Jean and Kingdon, Geeta, 2000. “School Participation in Rural India,” Review of Development Economics.
  9. Delavari, N., Beikzadeh, M.R. (2004). A new model for using data mining in higher education system, 5th international Conference on Information Technology based Higher education and training: ITEHT (04), Istanbul, Turkey, 31st m\May-2nd June 2004.
  10. Das J,S, dercon , J Habyarimana ,P Krishna ,K Muralidharana and V.sundararaman 2013 “ School Input ,hose hold substitution and test scores” American Economic Journal Applied Economics 5(2), p-29-29.
  11. Ghaida Abu, D, and Stephan Klasen (2004), The Costs of Missing the Millennium Development Goal on Gender Equality.” World Development32 (7): 1075–107
  12. Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed.). Boston, MA: Elsevier.
  13. Johnstone, James and Jiyono. 1983. “Out-of-school Factors and Educational Achievement in Indonesia.” Comparative Education Review, 27(2), pp. 278-295.
  14. Jaiwei Han and Micheline Kamber, 2008 “Data Mining Concepts and Techniques”, Second Edition Morgan Kaufmann Publishers.
  15. James, Estelle, Elizabeth M King, and Ace Suryadi. 1996. "Finance, Management, and Costs of Public and Private Schools in Indonesia." Economics of Education Review, 15(4), pp. 387-98
  16. Jayaraman, Rajshri, Dora Simroth, and Francis De Vericourt. 2010. The Impact of School Lunches on Primary School Enrollment: Evidence from India's Mid-Day Meal Scheme. Indian Statistical Institute.
  17. Kotsiantis, S., Pierrakeas, C., Pintelas, P. 2004. Preventing student dropout in distance  learning systems using machine learning techniques, In International Conference on Knowledge-Based Intelligent Information & Engineering Systems, Oxford, 3-5.
  18. Khan Z, Khan N 2005. “Scholastic Achievement of Higher Secondary Students in Science Stream”, Journal of Social Sciences, Vol. 1, No. 2, pp. 84-87.

Publication Details

Published in : Volume 2 | Issue 1 | January-February 2016
Date of Publication : 2015-01-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 131-133
Manuscript Number : IJSRSET162140
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Manmohan Singh, Harish Nagar, Anjali Sant, " K-mean and EM Clustering algorithm using attendance performance improvement Primary school Student, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 1, pp.131-133, January-February-2016.
Journal URL : http://ijsrset.com/IJSRSET162140

Article Preview