  Suppose, we are given a set of $n$ elements to be clustered into $k$
(unknown) clusters, and an oracle/expert labeler that can interactively answer
pair-wise queries of the form, "do two elements $u$ and $v$ belong to the same
cluster?". The goal is to recover the optimum clustering by asking the minimum
number of queries. In this paper, we initiate a rigorous theoretical study of
this basic problem of query complexity of interactive clustering, and provide
strong information theoretic lower bounds, as well as nearly matching upper
bounds. Most clustering problems come with a similarity matrix, which is used
by an automated process to cluster similar points together. Our main
contribution in this paper is to show the dramatic power of side information
aka similarity matrix on reducing the query complexity of clustering. A
similarity matrix represents noisy pair-wise relationships such as one computed
by some function on attributes of the elements. A natural noisy model is where
similarity values are drawn independently from some arbitrary probability
distribution $f_+$ when the underlying pair of elements belong to the same
cluster, and from some $f_-$ otherwise. We show that given such a similarity
matrix, the query complexity reduces drastically from $\Theta(nk)$ (no
similarity matrix) to $O(\frac{k^2\log{n}}{\cH^2(f_+\|f_-)})$ where $\cH^2$
denotes the squared Hellinger divergence. Moreover, this is also
information-theoretic optimal within an $O(\log{n})$ factor. Our algorithms are
all efficient, and parameter free, i.e., they work without any knowledge of $k,
f_+$ and $f_-$, and only depend logarithmically with $n$. Along the way, our
work also reveals intriguing connection to popular community detection models
such as the {\em stochastic block model}, significantly generalizes them, and
opens up many venues for interesting future research.
