  In this paper we present deterministic analysis of sufficient conditions for
sparse subspace clustering under missing data, when data is assumed to come
from a Union of Subspaces (UoS) model. In this context we consider two cases,
namely Case I when all the points are sampled at the same co-ordinates, and
Case II when points are sampled at different locations. We show that results
for Case I directly follow from several existing results in the literature,
while results for Case II are not as straightforward and we provide a set of
dual conditions under which, perfect clustering holds true. We provide
extensive set of simulation results for clustering as well as completion of
data under missing entries, under the UoS model. Our experimental results
indicate that in contrast to the full data case, accurate clustering does not
imply accurate subspace identification and completion, indicating the natural
order of relative hardness of these problems.
