Centroid Ratio for a Pairwise Random Swap Clustering Algorithm

Abstract—Centroid Ratio for a Pairwise Random Swap Clustering Algorithm. Clustering algorithm and cluster validity are two highly correlated parts in cluster analysis. In this paper, a novel idea for cluster validity and a clustering algorithm based on the validity index are introduced. A Centroid Ratio is firstly introduced to compare two clustering results. This centroid ratio is then used in prototype-based clustering by introducing a Pairwise Random Swap clustering algorithm to avoid the local optimum problem of k -means. The swap strategy in the algorithm alternates between simple perturbation to the solution and convergence toward the nearest optimum by k -means. The centroid ratio is shown to be highly correlated to the mean square error (MSE) and other external indices. Moreover, < Final Year Projects > it is fast and simple to calculate. An empirical study of several different datasets indicates that the proposed algorithm works more efficiently than Random Swap, Deterministic Random Swap, Repeated k-means or k-means++. The algorithm is successfully applied to document clustering and color image quantization as well.