Research Article

Data Mining Techniques Applying on Educational Dataset to Evaluate Learner Performance Using Cluster Analysis

Minimol Anil Job
* Corresponding author
DOI icon 10.24018/ejeng.2018.3.11.966

Read Counter
1373

Downloads
707

Citations

Share

Article Sidebar

Submitted 2018-10-31
Published 2018-11-21

Read counter = 1373 times

Table of Contents

Article Main Content

Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques. Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. Understanding the business value of the collected data it can be used for classifying and predicting the students’ behavior, academic performance, dropout rates, and monitoring progression and retention. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.
Keywords: Data Mining Educational Data Mining clustering cluster analysis performance evaluation prediction

References

  1. Aggarwal, C. Charu and Yu, S. Philip. “Data Mining Techniques for Associations, Clustering and Classification.” in Zhong, Ning and Zhou, Lizhu (Eds.) methodologies for knowledge discovery and data mining, third pacific Asia Conference, PAKDD, Beijing, China, April 26-28, 1999 proceedings, Springer, New York
     Google Scholar
  2. Alex Berson, Stephen Smith, and Kurt Thearling ,An Overview of Data Mining Techniques Excerpted from the book Building Data Mining Applications for CRM
     Google Scholar
  3. Anand, S., and Buchner, A. 1998. Decision Support Using Data Mining. Financial Times Pitman
     Google Scholar
  4. Baker, R.S.J.D. and Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1, 3-16.
     Google Scholar
  5. Berkhin, Pavel. "A survey of clustering data mining techniques." Grouping multidimensional data. Springer Berlin Heidelberg, 2006. 25-71.
     Google Scholar
  6. Berry, J.A. Michael and Linoff S. Gordon., “Data Mining Techniques for Marketing, Sales, and Customer Relationship Management.” Second Edition, Wiley Publishing, 2004
     Google Scholar
  7. Berthold, M. “Fuzzy Logic.” In M. Berthold and D. Hand (eds.), Intelligent Data Analysis. Milan: Springer, 1999
     Google Scholar
  8. Bradshaw, J.A., Carden, K.J., Riordan, D., 1991. Ecological ―Applications Using a Novel Expert System Shell‖. Comp. Appl. Biosci. 7, 79–83.
     Google Scholar
  9. Brijesh Kumar Baradwaj, Saurabh Pal, Data mining: machine learning, statistics, and databases, 1996.
     Google Scholar
  10. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., and Zanasi, A. 1998. Discovering Data Mining: From Concepts to Implementation, Prentice Hall Saddle River, New Jersey
     Google Scholar
  11. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing, vols. 1, 2. Cambridge, MA: MIT Press, 1986.
     Google Scholar
  12. D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
     Google Scholar
  13. Dan Pelleg and Andrew Moore. X-means: Extending K-means with Efficient Estimation of the Number of Clusters. ICML, 2000.
     Google Scholar
  14. Daniel B.A. ， Ping Chen Using Self-Similarity to Cluster Large Data Sets[J].Data Mining and Knowledge Discovery，2003，7(2):123-152.
     Google Scholar
  15. Danon, L., Diaz-Guilera, A., and Arenas, A. Effect of Size Heterogeneity on Community Identification in Complex Networks, J. Stat. Mech. P11010 (2006)
     Google Scholar
  16. Douglas H. Fisher. Iterative optimization and simplification of hierarchical clustering. Journal of Artificial Intelligence Research, 4:147–179, 1996.
     Google Scholar
  17. Esehrieh S. Jingwei Ke, Hall L. O. etc. Fast accurate fuzzy clustering through data reduction IEEE Transactions on Fuzzy systems，2003，11(2):262-270.
     Google Scholar
  18. Fayyad, U., Piatesky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), 1996. Advances in Knowledge Discovery and Data Mining, AAAI Press, Cambridge
     Google Scholar
  19. Gorunescu, Florin. Data Mining; Concepts, Models and Techniques, Springer-Verlag Berlin Heidelberg, 2011
     Google Scholar
  20. Guo, W. William, “Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction.” Expert System with Applications, 37 (2010) 3358-3365
     Google Scholar
  21. Han, J., Kamber, M. & Pei, J. (2011). 'Data Mining: Concepts and Techniques,' Morgan Kaufmann, San Francisco, CA.
     Google Scholar
  22. Han, J., Kamber, M. 2012. Data Mining: Concepts and Techniques, 3rd ed, 443-491
     Google Scholar
  23. J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2000.
     Google Scholar
  24. J. O. Omolehin, J. O. Oyelade, O. O. Ojeniyi and K. Rauf, “Application of Fuzzy logic in decision making on students’ academic performance,” Bulletin of Pure and Applied Sciences, vol. 24E(2), pp. 281-187, 2005
     Google Scholar
  25. Jiawei Han and Micheline Kamber (2006), Data Mining: Concepts and Techniques, The MorganKaufmann/Elsevier India
     Google Scholar
  26. Kantardzic, Mehmed. Data Mining: Concepts, Models, Methods and Algorithm, Second Edition, John Wiley and Sons, New Jersey, 2011
     Google Scholar
  27. Kriegel, Hans-Peter; Kröger, Peer; Sander, Jörg; Zimek, Arthur (2011). "Density-based Clustering". WIREs Data Mining and Knowledge Discovery 1 (3): 231–240.doi:10.1002/widm.30.
     Google Scholar
  28. Kumar, Varun, and Anupama Chadha. "An empirical study of the applications of data mining techniques in higher education." International Journal of Advanced Computer Science and Applications 2.3 (2011).
     Google Scholar
  29. Larose, T. Daniel. Discovering knowledge in data: An Introduction to Data Mining Techniques, John Wiley and Sons, New Jersey, 2005
     Google Scholar
  30. Luan, Jing. Data mining and its applications in higher education, new directions for institutional research, No. 113, 2002, Springer
     Google Scholar
  31. M. Srinivas and C. Krishna Mohan, “Efficient Clustering Approach using Incremental and Hierarchical Clustering Methods”, 2010 IEEE
     Google Scholar
  32. Mano, M.Morris and Charles R.Kine. Logic and Computer design Fundamentals, Third edition .Prentice Hall, and 2004.p.73
     Google Scholar
  33. Nisbet, Robert and Elder, John and Miner, Gary., Handbooks of Statistical analysis and Data Mining Applications, Academic Press Publications, 2009
     Google Scholar
  34. Nkitaben Shelke, Shriniwas Gadage,”A Survey of Data Mining Approaches in Performance Analysis and Evaluation”, (2015), International Journal of Advanced Research in Computer Science and Software Engineering
     Google Scholar
  35. Peña-Ayala, Alejandro. "Educational data mining: A survey and a data mining-based analysis of recent works." Expert systems with applications 41.4 (2014): 1432-1462. Publishers, London
     Google Scholar
  36. R. J. Mammone and Y. Y. Zeevi, Neural Networks: Theory and Applications. San Diego, CA: Academic, 1991
     Google Scholar
  37. Rajni Jindal and Malaya Dutta Borah, “A SURVEY ON EDUCATIONAL DATA MINING AND RESEARCH TRENDS, International Journal of Database Management Systems ( IJDMS ) Vol.5, No.3, June 2013.
     Google Scholar
  38. Raymond T. Ng and Jiawei Han. Efficient and effective clustering methods for spatial data mining. In Proc. of the VLDB Conference, Santiago, Chile, September 1994
     Google Scholar
  39. Renza Campagni, Donatella Merlini, Renzo Sprugnoli, Maria Cecilia Verri “Data mining models for student careers”, at Science Direct Expert Systems with Applications,pp55085521,2015,www.elsevier.com.
     Google Scholar
  40. Rokach, Lior. “A survey of clustering algorithm,” in O.Maimon, L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook, 2nd Edition, Springer, 2010
     Google Scholar
  41. Romero, C. & Ventura, S. (2007). "Educational Data Mining: A Survey from 1995 to 2005," Expert Systems with Applications, 33(1), 135-146.
     Google Scholar
  42. Romero, C., Ventura, S. & Garcia, E. (2008). "Data Mining in Course Management Systems: Moodle Case Study and Tutorial," Computers & Education, 51(1), 368-384.
     Google Scholar
  43. Romero, Cristóbal, and Sebastián Ventura. "Educational data mining: a review of the state of the art." Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 40, no. 6 (2010): 601-618
     Google Scholar
  44. Romero, Cristóbal, Sebastián Ventura, Pedro G. Espejo, and César Hervás. "Data Mining Algorithms to Classify Students." In EDM, pp. 8-17. 2008.
     Google Scholar
  45. S. Archna, Dr. K. Elangovan ―Survey of Classification Techniques in Data Mining‖, International Journal of Computer Science and Mobile Applications vol 2, Issue 2,, February 2014, p.g. 65-71
     Google Scholar
  46. Soni, Neha, and Amit Ganatra. "Comparative study of several Clustering Algorithms." International Journal of Advanced Computer Research (IJACR)(2012): 37-42. Systems with Applications, Vol. 33, 2007, 135-146.
     Google Scholar
  47. The McCULLOCH- Pitts Model by Samantha Hayman
     Google Scholar
  48. U. Fayadd, Piatesky, G. Shapiro, and P. Smyth, From data mining to knowledge discovery in databases, AAAI Press / The MIT Press, Massachusetts Institute Of Technology. ISBN 0–262 56097–6, 1996.
     Google Scholar
  49. Veenman C.J.，Reinders M.J.T.，Backer E. A maximum variance cluster algorithm，[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2002，24(9):1273-1280.
     Google Scholar
  50. Weiss, M. Sholom and Indurkhya, Nitin. Predictive Data Mining: A practical guide, Morgan Kauffmann Publishers, USA,
     Google Scholar