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Clustering Proficient Students Using Data Mining Approach

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Advances in Computing and Data Sciences (ICACDS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 721))

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Abstract

Every educational institution strives to be the best in terms of quality. Quality is measured using many parameters. One such parameter of measuring quality is proficient students. Hence the objective of this study is to Cluster proficient students of an educational institution using data mining approach. Clustering is based on knowledge, skill and ability concept known as KSA. A model and an algorithm are proposed to accomplish the task of Clustering. A student data set consisting of 1,434 students from an institution located in Bangalore are collected for the study and were subjected to preprocessing. To evaluate the performance of the proposed algorithm, it is compared with other Clustering algorithm on the basis of precision and recall. The results obtained are tabulated. The performance of the proposed algorithm was better in comparison with other algorithms.

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References

  1. Kim, S.Y., Jung, T.S., Suh, E.H., Hwang, H.S.: Student segmentation and strategy development based on student lifetime value: a case study. Expert Syst. Appl. 31(1), 101–107 (2006)

    Article  Google Scholar 

  2. Shin, H.W., Sohn, S.Y.: Product differentiation and market segmentation as alternative marketing strategies. Expert Syst. Appl. 27(1), 27–33 (2004)

    Article  Google Scholar 

  3. Jang, S.C., Morrison, A.M.T., O’Leary, J.T.: Benefit segmentation of Japanese pleasure travelers to the USA and Canada: selecting target markets based on the profitability and the risk of individual market segment. Tour. Manag. 23(4), 367–378 (2002)

    Article  Google Scholar 

  4. Hruschka, H., Natter, M.: Comparing performance of feed forward neural nets and k-means of cluster-based market segmentation. Eur. J. Oper. Res. 114(3), 346–353 (1999)

    Article  MATH  Google Scholar 

  5. Bottou, L., Bengio, Y.: Convergence Properties of the K-Means Algorithms. In: Advances in Neural Information Processing Systems 7 (1995)

    Google Scholar 

  6. Fabere, V.: Clustering and the continuous k-means algorithm. Los Alamos Sci. (1994)

    Google Scholar 

  7. Bydovska, H., Popelinsky, L.: Predicting student performance in higher education. In: 2013 24th International Workshop on Database and Expert Systems Applications (DEXA), pp. 141–145. IEEE (2013)

    Google Scholar 

  8. Archer-North and Associates: Performance Appraisal (2006). http://www.performance-appraisal.com. Accessed Dec 2012

  9. Chein, C., Chen, L.: Data mining to improve personnel selection and enhance human capital: a case study in high technology industry. Expert Syst. Appl. (2006, in Press)

    Google Scholar 

  10. Pal, K., Pal, S.: Analysis and mining of educational data for predicting the performance of students. (IJECCE) Int. J. Electron. Commun. Comput. Eng. 4(5), 1560–1565 (2013). ISSN: 2278-4209

    Google Scholar 

  11. Khan, Z.N.: Scholastic achievement of higher secondary students in science stream. J. Soc. Sci. 1(2), 84–87 (2005)

    Article  Google Scholar 

  12. Bharadwaj, B.K., Pal, S.: Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2(6), 63–69 (2011)

    Google Scholar 

  13. Bray, M.: The Shadow Education System: Private Tutoring and Its Implications for Planners, 2nd edn. UNESCO, Paris (2007)

    Google Scholar 

  14. Yadav, S.K., Bharadwaj, B.K., Pal, S.: Data mining applications: a comparative study for predicting student’s performance. (IJITCE) 1(12), 13–19 (2011)

    Google Scholar 

  15. Jamesmanoharan, J., Hari Ganesh, S., Felciah, M., Shafreenbanu, A.K.: Discovering students’ academic performance based on GPA using k-means clustering algorithm. In: 2014 World Congress on Computing and Communication Technologies (WCCCT), pp. 200–202. IEEE (2014)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Teachers Academy, Bangalore, India

    M. V. Ashok

  2. Department of MCA, GIMS, Bangalore, India

    A. Apoorva

Authors
  1. M. V. Ashok
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  2. A. Apoorva
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Corresponding author

Correspondence to A. Apoorva .

Editor information

Editors and Affiliations

  1. Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

    Mayank Singh

  2. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Raghogarh, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Indira Gandhi Delhi Technical University, New Delhi, India

    Arun Sharma

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada

    Tuncer Ören

  6. Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, Michigan, USA

    William Grosky

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© 2017 Springer Nature Singapore Pte Ltd.

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Ashok, M.V., Apoorva, A. (2017). Clustering Proficient Students Using Data Mining Approach. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_8

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  • DOI: https://doi.org/10.1007/978-981-10-5427-3_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5426-6

  • Online ISBN: 978-981-10-5427-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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