artlib.cvi
Cluster validity indices are metrics used to evaluate the quality of clustering results. These indices help to determine the optimal number of clusters and assess the performance of clustering algorithms by measuring the compactness and separation of the clusters. Common cluster validity indices include the Silhouette score, Davies-Bouldin index, and Dunn index. These indices play an important role in unsupervised learning tasks where true labels are not available for evaluation.
This module implements CVI-driven ART modules which utilize the CVI to inform clustering; often resulting in objectively superior results.