Title:
		"Nonparametric Clustering for Data Mining"

	Abstract:
		The use of density estimation to find clusters in
		data is supplementing ad hoc hierarchical
		methodology.  Examples include finding high-density
		regions, finding modes in a kernel density
		estimator, and the mode tree.  Alternatively,
		a mixture model may be fit and the mixture
		components associated with individual clusters.
		Fitting a high-dimensional mixture model with
		many components is difficult to estimate in
		practice.  Here, we survey mode and level set
		methods for finding clusters.  We describe a new
		algorithm that estimates a subset of a mixture
		model.  In particular, we demonstrate how to fit one
		component at a time and how the fits may be
		organized to reveal the complete clustering model.