@inproceedings{f21046a290914b35891ceca883141899,
title = "Using cluster ensemble to improve classification of student dropout in Thai university",
abstract = "Dropout or ceasing study prematurely has been widely recognized as a serious issue, especially in the university level. A large number of higher education institutes are facing the common difficulty with low rate of graduations in comparison to the number of enrollment. As compared to western countries, this subject has attracted only a few studies in Thai university, with educational data mining being limited to the use of conventional classification models. This paper presents the most recent investigation of student dropout at Mae Fah Luang University, Thailand, and the novel reuse of link-based cluster ensemble as a data transformation framework for more accurate prediction. The empirical study on students' personal, academic performance and enrollment data, suggests that the proposed approach is usually more effective than several benchmark transformation techniques, across different classifiers.",
keywords = "classification, cluster ensemble, educational data mining, student dropout",
author = "Natthakan Iam-On and Tossapon Boongoen",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014 ; Conference date: 03-12-2014 Through 06-12-2014",
year = "2014",
month = feb,
day = "18",
doi = "10.1109/SCIS-ISIS.2014.7044875",
language = "English",
series = "2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014",
publisher = "IEEE Press",
pages = "452--457",
booktitle = "2014 Joint 7th International Conference on Soft Computing and Intelligent Systems, SCIS 2014 and 15th International Symposium on Advanced Intelligent Systems, ISIS 2014",
address = "United States of America",
}