  Clustering is an essential data mining tool that aims to discover inherent
cluster structure in data. As such, the study of clusterability, which
evaluates whether data possesses such structure, is an integral part of cluster
analysis. Yet, despite their central role in the theory and application of
clustering, current notions of clusterability fall short in two crucial aspects
that render them impractical; most are computationally infeasible and others
fail to classify the structure of real datasets.
  In this paper, we propose a novel approach to clusterability evaluation that
is both computationally efficient and successfully captures the structure of
real data. Our method applies multimodality tests to the (one-dimensional) set
of pairwise distances based on the original, potentially high-dimensional data.
We present extensive analyses of our approach for both the Dip and Silverman
multimodality tests on real data as well as 17,000 simulations, demonstrating
the success of our approach as the first practical notion of clusterability.
