Distance matrix pre-caching and distributed computation of internal validation indices in k-medoids clustering
In this paper we discuss techniques for potential speedups in k-medoids clustering. Specifically, we address the advantages of pre-caching the pairwise distance matrix, heart of the k-medoids clustering algorithm, not only in order to speedup the execution of the algorithm itself, but also in order to speedup the evaluation of the well-known Silhouette Index and Davies-Bouldin Index for clusters’ validation. A major disadvantage of such pre-caching is that it might not be suitable for large datasets.