A Conceptual Subspace Clustering Algorithm in e-Learning
10th International Conference on Advanced Communication Technology (ICACT 2008)
In recent years, due to large amounts of network-based teaching and learning data continue to grow inexorably in size and complexity, knowledge clustering becomes more important in e-learning. This paper proposes a novel algorithm of cluster analysis to extract clusters in dense sub- spaces and the clusters can be described by overlapping hierarchical concepts. The experimental results show the algorithm is efficient to extract conceptual clusters in large data.