Uncertainty clustering internal validity assessment using Fréchet distance for unsupervised learning

ABSTRACT: Knowing the number of clusters a priori is one of the most challenging aspects of unsupervised learning. Clustering Internal Validity Indices (CIVIs) evaluate partitions in unsupervised algorithms based on metrics like compactness, separation, and density. However, specialized CIVIs for sp...