Before revision:  Discovering the subspace that defines clusters provides low dimensional representations of the data which capture cluster diversity, and provides greater understanding of the system by identifying the key underlying variables. Here, we define a class of problems in which linear separability of clusters is hidden in a low dimensional space.
After revision:  Discovering the subspace that defines clusters provides low dimensional representations of the data points. We define a class of problems in which linear separability of clusters is hidden in a low dimensional space.