A method for initialising the K-means clustering algorithm using kd-trees

We present a method for initialising the K-means clustering algorithm. Our method hinges on the use of a kd-tree to perform a density estimation of the data at various locations. We then use a modi cation of Katsavounidis' algorithm, which incorporates this density information, to choose K seeds for the K-means algorithm. We test our algorithm on 36 synthetic data sets and compare with 25 runs of Forgy's random initialisation method.