Block Probabilistic Distance Clustering : A Unified Framework and Evaluation

Probabilistic Distance (PD) clustering is a flexible and widely studied method in cluster analysis, owing to its probabilistic framework that combines distance measures with cluster membership probabilities. Building on this approach, we propose a novel block clustering framework and algorithm. The proposed algorithm is validated using both non-parametric distances, such as Squared Euclidean and Squared Mahalanobis distances, and parametric probabilistic distances derived from Gaussian and location Scale t-distributions for continuous data. To evaluate the clustering performance of the proposed algorithms, we modified the existing Extended Silhouette Index and used it alongside the established Co-clustering Adjusted Rand Index for comparison. This comprehensive evaluation highlights the effectiveness of our framework in advancing block clustering methodologies. JEL Classification: C38 MSC Classification: 62H30