A competitive elliptical clustering algorithm

This paper introduces a new learning algorithm for on-line ellipsoidal clustering. The algorithm is based on the competitive clustering scheme extended by two specific features. Elliptical clustering is accomplished by efficiently incorporating the Mahalanobis distance measure into the learning rules, and underutilization of smaller clusters is avoided by incorporating a frequency-sensitive term. Experiments are conducted to demonstrate the usefulness of the algorithm on artificial datasets as well as on the problem of texture segmentation.