Features extraction to improve performance of clustering process on student achievement

Abstract

In clustering data, there are two popular methods which are usually used: k-Means and Fuzzy C Means (FCM). Clustering process by these two methods, however, are sometimes influenced by the data suitable being used. This may affect the performance, for example: execution time, accuracy level. In order to overcome this problem, especially in a student evaluation system, we propose a feature extraction stage, which is implemented in the data preprocessing before being used by FCM. This extraction itself is performed based on the category and the Bloom's Taxonomy by collecting student data in a serious game. The experimental results show that these proposed methods are able to increase the accuracy level and to reduce the execution time. In terms of accuracy, our method is, on average, 2.3-4.7% higher than that of the original FCM. In terms of the execution time, the proposed FCM is, on average, 2.2-2.7 second faster than the original.