Performance functions and clustering algorithms

Abstract

We investigate the effect of different performance functions for measuring the performance of clustering algorithms and derive different algorithms depending on which performance algorithm is used. In particular, we show that two algorithms may be derived which do not exhibit the dependence on initial conditions (and hence the tendency to get stuck in local optima) that the standard K-Means algorithm exhibits.