Silhouette - Proximity Measure Based Diagnostics for Standard, Soft, and Multi-Way Clustering

Quantifies clustering quality by measuring both cohesion within clusters and separation between clusters. Silhouette widths are computed from the n by K matrices that clustering algorithms return (distances or similarities to cluster prototypes, fuzzy memberships, or posterior probabilities) instead of all pairwise dissimilarities, for the partition an algorithm produced or one supplied by the user. Implements the simplified silhouette of Van der Laan et al. (2003) <doi:10.1080/0094965031000136012>, the probability of alternative cluster normalization of Raymaekers and Rousseeuw (2022) <doi:10.1080/10618600.2022.2050249>, the fuzzy silhouette average of Campello and Hruschka (2006) <doi:10.1016/j.fss.2006.07.006>, the density-based silhouette of Menardi (2011) <doi:10.1007/s11222-010-9169-0>, the soft silhouettes of Bhat Kapu and Kiruthika (2024) <doi:10.1080/23737484.2024.2408534>, and the extended silhouette for multi-way clustering of Schepers et al. (2008) <doi:10.1007/s00357-008-9005-9> and Bhat Kapu and Kiruthika (2025) <doi:10.21203/rs.3.rs-6973596/v1>. Provides summaries and 'ggplot2' silhouette plots in the style of Rousseeuw (1987) <doi:10.1016/0377-0427(87)90125-7>.