  Confusion matrices and derived metrics provide a comprehensive framework for
the evaluation of model performance in machine learning. These are well-known
and extensively employed in the supervised learning domain, particularly
classification. Surprisingly, such a framework has not been fully explored in
the context of clustering validation. Indeed, just recently such a gap has been
bridged with the introduction of the Area Under the ROC Curve for Clustering
(AUCC), an internal/relative Clustering Validation Index (CVI) that allows for
clustering validation in real application scenarios. In this work we explore
the Area Under Precision-Recall Curve (and related metrics) in the context of
clustering validation. We show that these are not only appropriate as CVIs, but
should also be preferred in the presence of cluster imbalance. We perform a
comprehensive evaluation of proposed and state-of-art CVIs on real and
simulated data sets. Our observations corroborate towards an unified validation
framework for supervised and unsupervised learning, given that they are
consistent with existing guidelines established for the evaluation of
supervised learning models.

Machine Learning