A uniformity criterion and algorithm for data clustering

We propose a novel multivariate uniformity criterion for testing uniformity of point density in an arbitrary dimensional point pattern. An unsupervised, nonparametric data clustering algorithm, using this criterion, is also presented. The algorithm relies on a relatively general notion of cluster so that it is applicable to clusters of relatively unrestricted shapes, densities and sizes. We define a cluster as a set of contiguous interior points surrounded by border points. We use our uniformity test to differentiate between interior and border points. We group interior points to form cluster cores, and then identify cluster borders as formed by the border points neighboring the cluster cores. The algorithm is effective in resolving clusters of different shapes, sizes and densities. It is relatively insensitive to outliers. We present results for experiments performed on artificial and real data sets.