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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/2356

Title: Data clustering using higher order statistics
Authors: RAJAGOPALAN, AN
YEASIN, M
CHAUDHURI, S
Issue Date: 1997
Publisher: IEEE
Citation: IEEE TENCON'97 - IEEE REGIONAL 10 ANNUAL CONFERENCE, PROCEEDINGS, VOLS 1 AND 2: SPEECH AND IMAGE TECHNOLOGIES FOR COMPUTING AND TELECOMMUNICATIONS,803-806
Abstract: Traditional k-means algorithms for data clustering are based on the assumption that the underlying distribution of the data is Gaussian. In this paper, we propose a new clustering algorithm that makes use of higher order statistics for improved data clustering when the distribution of the data is non-Gaussian. The algorithm uses an HOS-based decision measure which is derived from a series expansion of the multivariate probability density function in terms of the multivariate Gaussian and the Hermite polynomials. Experimental results are presented on the performance of the proposed algorithm.
URI: http://dspace.library.iitb.ac.in/xmlui/handle/10054/15715
http://hdl.handle.net/100/2356
ISBN: 0-7803-4365-4
ISSN: 0886-1420
Appears in Collections:Proceedings papers

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