Use the information from the previous iteration to reduce the number of distance calculations. It uses a heuristic which determines if the closest prototype of a pattern q has been changed or not by using a simple check. If the assignment has not changed, no further distance calculations are required. Large-Scale Parallel Data Clustering . Int'l Conference on Pattern Recognition, August 1996.
Cluster analysis is a very important and useful technique.
As a consequence, hundreds of clustering algorithms have been proposed in the literature and new clustering algorithms continue to appear. However , many of these so called enhancements to the K means algorithm are computationally demanding and require additional user speci ed parameters for which no general guidelines are available. Judd et al. Pattern Analysis and Machine Intelligence, vol. Our goal was to prune as much computation as possible while preserving the clustering quality.