Split-Merge Incremental LEarning (SMILE) of mixture models

Abstract

In this article we present an incremental method for building a mixture model. Given the desired number of clusters K ≥ 2, we start with a two-component mixture and we optimize the likelihood by repeatedly applying a Split-Merge operation. When an optimum is obtained, we add a new component to the model by splitting in two, a properly chosen cluster. This goes on until the number of components reaches a preset limiting value. We have performed numerical experiments on several data-sets and report a performance comparison with other rival methods. © Springer-Verlag Berlin Heidelberg 2007.