Unsupervised Clustering using Multi-Resolution Perceptual Grouping

Abstract

Clustering is a common operation for data partitioning in many practical applications. Often, such data distributions exhibit higher level structures which are important for problem characterization, but are not explicitly discovered by existing clustering algorithms. In this paper, we introduce multi-resolution perceptual grouping as an approach to unsupervised clustering. Specifically, we use the perceptual grouping constraints of proximity, density, contiguity and orientation similarity. We apply these constraints in a multi-resolution fashion, to group sample points in high dimensional spaces into salient clusters. We present an extensive evaluation of the clustering algorithm against state-of-the-art supervised and unsupervised clustering methods on large datasets.