A conceptual subspace clustering algorithm in e-learning

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.