Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences

Low-Noise Density Clustering

Publication Date

July, 2009

Publication Type

Tech Report

Author(s)

Alessandro Rinaldo and Larry Wasserman

Abstract

We study density-based clustering under low-noise conditions. Our framework allows for sharply defined clusters such as clusters on lower dimensional manifolds. We show that accurate clustering is possible even in high dimensions. We propose two data-based methods for choosing the bandwidth and we study the stability properties of density clusters. We show that a simple graph-based algorithm known as the ``friends-of-friends'' algorithm successfully approximates the high density clusters.