To cluster, or not to cluster: An analysis of clusterability methods. (April 2019) - Adolfsson, Andreas; Ackerman, Margareta; Brownstein, Naomi C.
Highlights: The paper surveys and compares clusterability tests. New clusterability tests are proposed. Type I error and power of clusterability methods are reported for simulated data. Clusterablity tests are applied to well-known non-simulated data. Provide guidelines to help users to select among clusterability tests. Abstract: 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.