Proximity Measure Based Diagnostics for Standard, Soft, and Multi-Way Clustering
Compute Calculate of All Possible Silhouette Methods
Certainty Silhouette Width (Cer) for Soft Clustering
Density-Based Silhouette Width (DBS) for Soft Clustering
Calculate Extended Silhouette Width for Multi-Way Clustering
Create Silhouette Object from User Components
Check if Object is of Class Silhouette
Plot Silhouette Analysis Results
Silhouette: Proximity Measure Based Diagnostics for Standard, Soft, an...
Calculate Silhouette Widths, Summary, and Plot for Clustering Results
Calculate Silhouette Width for Soft Clustering Algorithms
Quantifies clustering quality by measuring both cohesion within clusters and separation between clusters. Implements advanced silhouette width computations for diverse clustering structures, including: simplified silhouette (Van der Laan et al., 2003) <doi:10.1080/0094965031000136012>, Probability of Alternative Cluster normalization methods (Raymaekers & Rousseeuw, 2022) <doi:10.1080/10618600.2022.2050249>, fuzzy clustering and silhouette diagnostics using membership probabilities (Campello & Hruschka, 2006; Menardi, 2011; Bhat & Kiruthika, 2024) <doi:10.1016/j.fss.2006.07.006>, <doi:10.1007/s11222-010-9169-0>, <doi:10.1080/23737484.2024.2408534>, and multi-way clustering extensions such as block and tensor clustering (Schepers et al., 2008; Bhat & Kiruthika, 2025) <doi:10.1007/s00357-008-9005-9>, <doi:10.21203/rs.3.rs-6973596/v1>. Provides tools for computation and visualization (Rousseeuw, 1987) <doi:10.1016/0377-0427(87)90125-7> to support robust and reproducible cluster diagnostics across standard, soft, and multi-way clustering settings.