Before revision:  We studied a simple algorithm we callData clustering using flats minimum distances , using the following assumptions: 1) There are m clusters. 2) Each cluster is modeled as a  ball in \mathbb{R}^d  . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.
After revision:  We studied a simple algorithm we callData clustering using flats minimum distances partial information and high dimension. Assuming the data set is separable, namely, its emergence from clusters that can be modeled as a ball in \mathbb{R}^d . 3) Each cluster contains a \frac{n{m}, k-dimensional affine subspaces . 4) All k-dimensional affine subspaces, which belong in the same cluster, are intersected with the ball of the cluster.