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Titolo:
Supervised cluster classification using the original n-dimensional space without transformation into lower dimension
Autore:
Al-Ammar, AS; Barnes, RM;
Indirizzi:
Univ Massachusetts, Lederle Grad Res Ctr Towers, Dept Chem, Amherst, MA 01003 USA Univ Massachusetts Amherst MA USA 01003 Dept Chem, Amherst, MA 01003 USA
Titolo Testata:
JOURNAL OF CHEMOMETRICS
fascicolo: 1, volume: 15, anno: 2001,
pagine: 49 - 67
SICI:
0886-9383(200101)15:1<49:SCCUTO>2.0.ZU;2-8
Fonte:
ISI
Lingua:
ENG
Soggetto:
MILK;
Keywords:
supervised cluster analysis; direct clustering in original space;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
17
Recensione:
Indirizzi per estratti:
Indirizzo: Barnes, RM Univ Massachusetts, Lederle Grad Res Ctr Towers, Dept Chem, Box34510, Amherst, MA 01003 USA Univ Massachusetts Box 34510 Amherst MA USA 01003 MA 01003 USA
Citazione:
A.S. Al-Ammar e R.M. Barnes, "Supervised cluster classification using the original n-dimensional space without transformation into lower dimension", J CHEMOMETR, 15(1), 2001, pp. 49-67

Abstract

A novel supervised classification algorithm, direct clustering in n-dimensional space (DCNS), was developed for difficult data sets where conventional methods of supervised clustering are expected to fail. The method is based, when applied on >3-dimensional spaces, on an algorithm that performs special treatment on the measurement space, so that the treated space can allow a computer-aided clustering methodology similar to that used by human vision, However, unlike other techniques that reduce the dimensionality of thespace, the proposed method preserves the original dimensions while performing a computer-simulated human vision clustering in the original n-dimensional space. Thus the overlap between clusters that results from the dimensionality reduction is eliminated. The proposed method was applied to two realdata sets. The results are compared with those obtained using principal component analysis (PCA), an artificial neural network (ANN), and the k-nearest-neighbor (KNN) technique. On one data set containing only two clusters, the DCNS algorithm gives better cluster separation than the other three methods. However, when all four methods were applied on the second data set, containing eight different clusters, PCA, ANN and KNN were unable to give useful cluster separation, while the DCNS method was able to separate all clusters and classify the unknown points successfully with their correspondingclusters. The DCNS technique is able to perform other important cluster analysis tasks, such as testing the discriminatory power of a variable, selecting one variable from many, and conducting preliminary unsupervised clustering. Copyright (C) 2000 John Wiley & Sons, Ltd.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 20/01/20 alle ore 10:44:19