  Given a set of points, clustering consists of finding a partition of a point
set into $k$ clusters such that the center to which a point is assigned is as
close as possible. Most commonly, centers are points themselves, which leads to
the famous $k$-median and $k$-means objectives. One may also choose centers to
be $j$ dimensional subspaces, which gives rise to subspace clustering. In this
paper, we consider learning bounds for these problems. That is, given a set of
$n$ samples $P$ drawn independently from some unknown, but fixed distribution
$\mathcal{D}$, how quickly does a solution computed on $P$ converge to the
optimal clustering of $\mathcal{D}$? We give several near optimal results. In
particular,
  For center-based objectives, we show a convergence rate of
$\tilde{O}\left(\sqrt{{k}/{n}}\right)$. This matches the known optimal bounds
of [Fefferman, Mitter, and Narayanan, Journal of the Mathematical Society 2016]
and [Bartlett, Linder, and Lugosi, IEEE Trans. Inf. Theory 1998] for $k$-means
and extends it to other important objectives such as $k$-median.
  For subspace clustering with $j$-dimensional subspaces, we show a convergence
rate of $\tilde{O}\left(\sqrt{\frac{kj^2}{n}}\right)$. These are the first
provable bounds for most of these problems. For the specific case of projective
clustering, which generalizes $k$-means, we show a convergence rate of
$\Omega\left(\sqrt{\frac{kj}{n}}\right)$ is necessary, thereby proving that the
bounds from [Fefferman, Mitter, and Narayanan, Journal of the Mathematical
Society 2016] are essentially optimal.
