Sequential clustering: tracking down the most natural clusters

Sequential superparamagnetic clustering (SSC) is a substantial extension of the superparamagnetic clustering approach (SC). We demonstrate that the novel method is able to master the important problem of inhomogeneous classes in the feature space. By fully exploiting the non-parametric properties of SC, the method is able to find the natural clusters even if they are highly different in shape and density. In such situations, concurrent methods normally fail. We present the results from a fully automated implementation of SSC (applications to chemical data and visual scene analysis) and provide analytical evidence of why the method works.