A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

Abstract

ABSTRACTExploiting single models for a better ensemble one is popular for a classification or clustering task. For educational data clustering, our work proposes a kernel-induced weighted object-cluster association ensemble method, named kWOCA, to discover the inherent structure of the data and return the student clusters of higher quality. kWOCA is an advanced version that can automatically determine the number of desired clusters based on Bayesian Information Criterion. It also conducts consensus clustering in the feature space for non-linearly separated clusters where the discrimination between the objects is captured from the base clusterings. Besides, it encodes the differences between the clusters in each base clustering and those between the base clusterings in the ensemble when making a synthesis of the base clusterings. A kernel-induced weighted object-cluster association matrix is defined to store such rich information. Using this matrix, kWOCA outperforms the existing clustering ensemble methods. Experimental results on the real educational data sets and the benchmark Iris data set show the better effectiveness of kWOCA with higher Normalized Mutual Information values. As a result, groups of the most similar students based on their study performance can be discovered better. These resulting student clusters can be then further analyzed for academic affairs.