The method is based on the above principle, and on the joint distance function, a measure of distance from all cluster centers, that evolves during the iterations, and captures the data in its low contours.
At each iteration, the distances (Euclidean, Mahalanobis, etc.) from the cluster centers are computed for all data points, and the centers are updated as stationary points of the joint distance function. The initial centers are arbitrary and computations stop when the centers stop moving.
The method is simple, fast (requiring a small number of cheap iterations) and gives a high percentage of correct classifications. It converges to the true cluster centers for all initial solutions, and is not sensitive to outliers.
Keywords: Distance clustering, probabilistic clustering, Euclidean
distance, Mahalanobis distance
Paper Available at: ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/2006/2006-09.ps.gz