A uniformity criterion and algorithm for data clustering

Abstract

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.

Supervised and Unsupervised Clustering with Probabilistic Shift

“…Cluster identification by connected components labeling of overlapping uniform neighborhoods has been proposed in [17]. However, as stated at the beginning of this section, this may lead to cluster splits and merges .…”

Count Data Clustering Using Unsupervised Localized Feature Selection and Outliers Rejection

“…In particular, clustering has been the topic of extensive research and has been applied in several domains [3], [4], [5], [6]. Finite mixture models have been widely and successfully applied for model-based clustering [7], [8].…”