Iterative refinement clustering algorithms (e.g. K-Means, EM) converge to one of numerous local minima. It is known that they are especially sensitive to initial conditions. We present a procedure for computing a refined starting condition from a given initial one that is based on an efficient technique for estimating the modes of a distribution.
The refined initial starting condition leads to convergence to “better” local minima. The procedure is applicable to a wide class of clustering algorithms for both discrete and continuous data.