Clustering is a key technique within the KDD process, with k-means, and the more general k-medoids, being well-known incremental partition-based clustering algorithms. A fundamental issue within this class of algorithms is to find an initial set of medians (or medoids) that improves the efficiency of the algorithms (e.g., accelerating its convergence to a solution), at the same time that it improves its effectiveness (e.g., finding more meaningful clusters). Thus, in this article we aim at providing a technique that, given a set of elements, quickly finds a very small number of elements as medoid candidates for this set, allowing to improve both the efficiency and effectiveness of existing clustering algorithms. We target the class of k-medoids algorithms in general, and propose a technique that selects a well-positioned subset of central elements to serve as the initial set of medoids for the clustering process. Our technique leads to a substantially smaller amount of distance calculations, thus improving the algorithm's efficiency when compared to existing methods, without sacrificing effectiveness.  A salient feature of our proposed technique is that it is not a new k-medoid clustering algorithm per se, rather, it can be used in conjunction with any existing clustering algorithm that is based on the k-medoid paradigm. Experimental results, using both synthetic and real datasets, confirm the efficiency, effectiveness and scalability of the proposed technique.
['Adriano Arantes Paterlini', 'Mario A. Nascimento', 'Caetano Traina']
Using Pivots to Speed-Up k-Medoids Clustering