Improving the Quality of the Clustering Process on Students’ Performance using Feature Selection

Abstract

the quality of students' performance clusters relates to the accuracy of students being in groups based on their performance. However, the resulting quality sometimes needs to be improved because the clustering process involves features that are not dominant. Furthermore, in the previous works, measurement of the quality of the clusters in unsupervised evaluation often only uses one measure. Therefore, this paper focuses to enhance the quality of clusters by eliminating features that are irrelevant by applying the feature selection method called the Gini Index. Meanwhile, in this paper, the clustering method applied is K-means for the mining process. Then, we propose the evaluation process measured by three metrics, namely: silhouette coefficient, ANOVA, and t-test. The experimental results show that the Gini Index can improve the quality of clusters based on the three proposed metrics.