  Clustering is an essential data mining tool that aims to discover inherent
cluster structure in data. For most applications, applying clustering is only
appropriate when cluster structure is present. As such, the study of
clusterability, which evaluates whether data possesses such structure, is an
integral part of cluster analysis. However, methods for evaluating
clusterability vary radically, making it challenging to select a suitable
measure. In this paper, we perform an extensive comparison of measures of
clusterability and provide guidelines that clustering users can reference to
select suitable measures for their applications.
