A fast approximated k–median algorithm

Abstract

The k-means algorithm is a well–known clustering method. Although this technique was initially defined for a vector representation of the data, the set median (the point belonging to a set P that minimizes the sum of distances to the rest of points in P) can be used instead of the mean when this vectorial representation is not possible. The computational cost of the set median is O(|P|2). Recently, a new method to obtain an approximated median in O(|P|) was proposed. In this paper we use this approximated median in the k–median algorithm to speed it up.