www.istkon.net / kayit@istkon.net

November 27, 2017

Dear M. Cem BABADOĞAN,

We are pleased to receive your abstract titled “Application of Fuzzy c-means Clustering

Algorithm for Prediction of Students’ Academic Performance” submitted to the 10th

International Statistics Congress (ISC2017) which is held on December 06-08, 2017 and organized

by Turkish Statistical Association and Ankara University.

Based on the evaluations done by the Congress Scientific Committee, I would like to inform you

that your abstract is classified as contributed paper presentation. The program and paper

presentation rules will be announced on the web site (www.istkon.net) soon and I recommend you

to visit the web site for updated information.

I thank you and express my appreciation for your support and collaboration on behalf of Turkish

Statistical Association.

Yours sincerely,

Professor Ayşen Apaydın

Chair of the Organizing Committee

http://www.istkon.net/mailto:kayit@istkon.netCem Babadoğan�

December 6-8, 2017 ANKARA/TURKEY

Application of Fuzzy c-means Clustering Algorithm for Prediction of

Students’ Academic Performance

Ayşen APAYDIN1, Furkan BAŞER1,Ömer KUTLU2, M. Cem BABADOĞAN2, Özge ALTINTAŞ2, Tuğba KUNDUROĞLU AKAR2

aysen.apaydin@ankara.edu.tr, furkan.baser@ankara.edu.tr omerkutlu@ankara.edu.tr, cem@ankara.edu.tr, oaltintas@ankara.edu.tr, tkunduroglu@education.ankara.edu.tr

1Faculty of Applied Sciences, Ankara University, 06590 Cebeci, Ankara, Turkey

2Faculty of Educational Sciences, Ankara University, 06590 Cebeci, Ankara, Turkey

Nowadays, the amount of data stored in educational database is rapidly increasing. These databases contain some information to improve the performance of students, which is influenced by many factors. Therefore, it is essential to develop a classification system so as to identify the difference between students (Oyelade et al., 2010). The main purpose of clustering is to find out the classification structure of the data. Clustering algorithms based on its structure are generally divided into two types: fuzzy and non-fuzzy (crisp) clustering (Gokten et al., 2017). Fuzzy clustering methods are used for calculating the membership function that determines to which degree the objects belong to clusters and used for detecting overlapping clusters in the data set (De Oliveira and Pedrycz, 2007). The aim of this study is to illustrate the use of a fuzzy c-means (FCM) clustering approach for application to the grouping of students into different clusters according to various factors. Utilizing a set of records for students who were registered at Ankara University in the academic year 2014-2015, it was determined that FCM clustering method gives remarkable results.

Keywords: academic performance, classification, fuzzy c-means

References

[1] De Oliveira, J.V. and Pedrycz, W. (2007), Advances in fuzzy clustering and its applications, West Sussex, Wiley.

[2] Gokten, P. O., Baser, F., and Gokten, S. (2017). Using fuzzy c-means clustering algorithm in financial health scoring. The Audit Financiar journal, 15(147), 385-385.

[3] Oyelade, O. J., Oladipupo, O. O., and Obagbuwa, I. C. (2010), Application of k-means clustering algorithm for prediction of Students Academic Performance, International Journal of Computer Science and Information Security, 7(1), 292-295.

Cem Babadoğan�

ISC 2017 Kabul 186ISC 2017ISC 2017 Katılım