Linear manifold clustering in high dimensional spaces by stochastic search

Classical clustering algorithms are based on the concept that a cluster center is a single point. Clusters which are not compact around a single point are not candidates for classical clustering approaches. In this paper we present a new clustering paradigm in which the cluster center is a linear manifold. Clusters are groups of points compact around a linear manifold. A linear manifold of dimension 0 is a point. So clustering around a center point is a special case of linear manifold clustering. Linear manifold clustering (LMCLUS) identifies subsets of the data which are embedded in arbitrary oriented lower dimensional linear manifolds. Minimal subsets of points are repeatedly sampled to construct trial linear manifolds of various dimensions. Histograms of the distances of the points to each trial manifold are computed. The sampling corresponding to the histogram having the best separation between a mode near zero and the rest is selected and the data points are partitioned on the b...

• Type: Journal
• Year: 2007
• Where: PR
• Authors: Robert M. Haralick, Rave Harpaz
• Keywords: Classical Clustering, Linear Manifold, Manifold, PR 2007