Accessibility navigation

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.

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.


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.

Item Type:Conference or Workshop Item (Paper)
ID Code:21640
Additional Information:Proceedings ISBN: 9780817639891

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation