  Sparse subspace clustering (SSC) is an elegant approach for unsupervised
segmentation if the data points of each cluster are located in linear
subspaces. This model applies, for instance, in motion segmentation if some
restrictions on the camera model hold. SSC requires that problems based on the
$l_1$-norm are solved to infer which points belong to the same subspace. If
these unknown subspaces are well-separated this algorithm is guaranteed to
succeed. The algorithm rests upon the assumption that points on the same
subspace are well spread. The question what happens if this condition is
violated has not yet been investigated. In this work, the effect of particular
distributions on the same subspace will be analyzed. It will be shown that SSC
fails to infer correct labels if points on the same subspace fall into more
than one cluster.
