A Hybrid Clustering Method for Identifying High-Density Clusters

Abstract

Abstract A hybrid method for clustering multivariate observations is proposed, which combines elements of the k-means and the single-linkage clustering techniques. One purpose of the proposed method is to discover the high-density clusters given a random sample of size N from some underlying population; a high-density cluster at level c in a population with density f is defined as a maximal connected set of points x with f(x) ≥ c. This clustering procedure is practicable for very large numbers of observations and is shown to be consistent, under certain regularity conditions, in one dimension.