Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
PARAMETRIC AND NONPARAMETRIC UNSUPERVISED CLUSTER-ANALYSIS
Autore:
ROBERTS SJ;
Indirizzi:
UNIV LONDON IMPERIAL COLL SCI TECHNOL & MED,DEPT ELECT & ELECT ENGN LONDON SW7 2BT ENGLAND
Titolo Testata:
Pattern recognition
fascicolo: 2, volume: 30, anno: 1997,
pagine: 261 - 272
SICI:
0031-3203(1997)30:2<261:PANUC>2.0.ZU;2-2
Fonte:
ISI
Lingua:
ENG
Soggetto:
TEXTURE SEGMENTATION; IMAGE SEGMENTATION; NEURAL NETWORK; ALGORITHMS;
Keywords:
CLUSTER ANALYSIS; MAXIMUM LIKELIHOOD METHODS; SCALE-SPACE FILTERING; PROBABILITY DENSITY ESTIMATION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
CompuMath Citation Index
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
33
Recensione:
Indirizzi per estratti:
Citazione:
S.J. Roberts, "PARAMETRIC AND NONPARAMETRIC UNSUPERVISED CLUSTER-ANALYSIS", Pattern recognition, 30(2), 1997, pp. 261-272

Abstract

Much work has been published on methods for assessing the probable number of clusters or structures within unknown data sets. This paper aims to look in more detail at two methods, a broad parametric method, based around the assumption of Gaussian clusters and the other a non-parametric method which utilises methods of scale-space filtering to extract robust structures within a data set. It is shown that, whilst both methods are capable of determining cluster validity for data sets inwhich clusters tend towards a multivariate Gaussian distribution, theparametric method inevitably fails for clusters which have a non-Gaussian structure whilst the scale-space method is more robust. Copyright(C) 1997 Pattern Recognition Society.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 07/07/20 alle ore 05:11:58