Subspace and projected clustering: experimental evaluation and analysis
Subspace and projected clustering have emerged as a possible solution to the challenges associated with clustering in high-dimensional data. Numerous subspace and projected clustering techniques have been proposed in the literature. A comprehensive evaluation of their advantages and disadvantages is urgently needed. In this paper, we evaluate systematically state-of-the-art subspace and projected clustering techniques under a wide range of experimental settings. We discuss the observed performance of the compared techniques, and we make recommendations regarding what type of techniques are suitable for what kind of problems.
projected clustering subspace clustering