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 subspaces 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.