A robust and effective algorithmic framework for incomplete educational data clustering

Data clustering is one of the popular tasks recently used in the educational data mining arena for grouping similar students by several aspects such as study performance, behavior, skill, etc. Many well-known clustering algorithms such as k-means, expectation-maximization, spectral clustering, etc. were employed in the related works. None of them has taken into consideration the incompleteness of the educational data gathered in an academic credit system. If just a few records have missing values, we might ignore them in the mining task. However, as there are a large number of missing values, ignoring them may lead to the data insufficiency and ineffectiveness of the mining task. Hence, we define a robust and effective algorithmic framework for incomplete educational data clustering using the nearest prototype strategy. Within the framework, we propose two novel incomplete educational data clustering algorithms K_nps and S_nps based on the k-means algorithm and the self-organizing map, respectively. Experimental results have shown that the clusters from our proposed algorithms have better cluster quality as compared to the different existing approaches.