UNSPECIFIED (1997) On merging gradient estimation with mean-tracking techniques for cluster identification THE CURSE OF DIMENSIONALITY. In: 2nd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing - Can We Beat the Curse of Dimensionality, AUG 28-30, 1996, PRAGUE, CZECH REPUBLIC.

Full text not available from this repository.

Abstract

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 Item (UNSPECIFIED)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering Q Science > QA Mathematics
Journal or Publication Title:	COMPUTER-INTENSIVE METHODS IN CONTROL AND SIGNAL PROCESSING
Publisher:	BIRKHAUSER BOSTON
ISBN:	0-8176-3989-6
Editor:	Warwick, K and Karny, M
Date:	1997
Number of Pages:	10
Page Range:	pp. 63-72
Publication Status:	Published
Title of Event:	2nd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing - Can We Beat the Curse of Dimensionality
Location of Event:	PRAGUE, CZECH REPUBLIC
Date(s) of Event:	AUG 28-30, 1996
URI:	http://wrap.warwick.ac.uk/id/eprint/17602

Data sourced from Thomson Reuters' Web of Knowledge

Request changes to a record

Actions (login required)

View Item