On merging gradient estimation with mean-tracking techniques for cluster identification

Fox, P. D., Sutanto, E. L. and Warwick, K. (1997) On merging gradient estimation with mean-tracking techniques for cluster identification. In: 2nd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing, 28-30 Aug 1996, Prague, Czech Republic, pp. 49-54.

Abstract/Summary

This paper discusses how numerical gradient estimation methods may be used in order to reduce the computational demands on a class of multidimensional clustering algorithms. The study is motivated by the recognition that several current point-density based cluster identification algorithms could benefit from a reduction of computational demand if approximate a-priori estimates of the cluster centres present in a given data set could be supplied as starting conditions for these algorithms. In this particular presentation, the algorithm shown to benefit from the technique is the Mean-Tracking (M-T) cluster algorithm, but the results obtained from the gradient estimation approach may also be applied to other clustering algorithms and their related disciplines.