Extended mean shift in handwriting clustering

The mean shift method, a simple iterative clustering procedure that shifts each data point to the average of data points in its neighborhood, is being extended in three ways: 1) the window parameter controlling the size of the neighborhood is automatically determined from the original distribution of points; 2) stable, hierarchical clustering is achieved by increasing the window parameter value in a deterministic manner; and 3) the guarantee of its convergence is rigorously proven. A critical comparison of the extended mean shift method to the k-means method and other hierarchical clustering methods is made using artificial 2D point distributions of overlapping Gaussians and half-circles with random noise. Moreover, the extended mean shift method has successfully been applied to the clustering of a wide variety of handwriting deformation in a 256-dimensional feature space