A two-phase educational data clustering method based on transfer learning and kernel K-means

Abstract

In this paper, we propose a two-phase educational data clustering method using transfer learning and kernel k-means algorithms for the student data clustering task on a small target data set from a target program while a larger source data set from another source program is available. In the first phase, our method conducts a transfer learning process on both unlabeled target and source data sets to derive several new features and enhance the target space. In the second phase, our method performs kernel k-means in the enhanced target feature space to obtain the arbitrarily shaped clusters with more compactness and separation. Compared to the existing works, our work are novel for clustering the similar students into the proper groups based on their study performance at the program level. Besides, the experimental results and statistical tests on real data sets have confirmed the effectiveness of our method with the better clusters.