A comparison of Various Distance Functions on K – Mean Clustering Algorithm

Abstract

In this paper, the algorithm of k- mean is studied by a comparison in a simulation. The performance of the algorithm is compared at different distance formulas, which are (Euclidian, cityblock and cosine distance) for different number of clusters. As a results, the numerical simulation for comparison shows that the performance of the algorithm was better at the distance of the cityblock, for the more the number of clusters.