Generalized Density Clustering

We study generalized density-based clustering in which sharply defined clusters such as clusters on lower dimensional manifolds are allowed. 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 successfully approximates the high density clusters.