Clustering Validation with The Area Under Precision-Recall Curves
This work addresses the problem of clustering validation for researchers and practitioners, offering an incremental extension of supervised learning metrics to unsupervised scenarios.
The authors tackled the lack of a comprehensive validation framework for clustering by exploring the Area Under Precision-Recall Curve as a Clustering Validation Index, showing it is effective and preferable for imbalanced clusters through evaluation on real and simulated datasets.
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.