Abstract

In the student clustering, the high cluster validity is very important because of this cause clarity a student in a cluster. Furthermore, it becomes easier for a teacher to do the best learning process. This paper focuses on the improvement of cluster validity applied by a suitable feature selection method, especially student's psychomotor domain. Here, we propose the feature selection by the random method. In addition, we apply k-means as the popular clustering method in educational data mining by the two initial of cluster center point: k-means++ and random. For cluster evaluation stage, silhouette coefficient is used on Manhattan distance. The experimental result indicates that feature selection is able to enhance the cluster validity which has shown that our methods have higher silhouette value than original k-means. In terms of the maximum silhouette value, our method can reach higher than original-kmeans++ and original-random on average 0.033-0.106. In terms of the minimum silhouette value, our method can achieve higher than original-kmeans++ and original-random on average 0.123-0.240.

Original languageEnglish
Title of host publication2018 International Conference on Information and Communications Technology, ICOIACT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages460-465
Number of pages6
ISBN (Electronic)9781538609545
DOIs
Publication statusPublished - 26 Apr 2018
Event1st International Conference on Information and Communications Technology, ICOIACT 2018 - Yogyakarta, Indonesia
Duration: 6 Mar 20187 Mar 2018

Publication series

Name2018 International Conference on Information and Communications Technology, ICOIACT 2018
Volume2018-January

Conference

Conference1st International Conference on Information and Communications Technology, ICOIACT 2018
Country/TerritoryIndonesia
CityYogyakarta
Period6/03/187/03/18

Keywords

  • clustering
  • psychomotor
  • silhouette
  • student
  • validity

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