Watershed on vector quantization for clustering of big data

Abstract

A method for clustering of large amounts of data is presented which is a sequenced composition of two algorithms: the former builds a partition of input space into Voronoi regions and the latter partitions them. First, a model of clusters as high density regions in input space is presented, then it is shown how a Voronoi partition and it's topological map (a) can be build and (b) used as a low complexity approximation of the input space. During the (b) step, the usage of "watershed" algorithm is presented which has been pre viously used for image segmentation, but it is its first application to a data space partition that is proposed by the authors.