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Titolo:
Constructing ultrametric and additive trees based on the L-1 norm
Autore:
Smith, TJ;
Indirizzi:
No Illinois Univ, Dept Educ Technol Res & Assessment, De Kalb, IL 60115 USA No Illinois Univ De Kalb IL USA 60115 & Assessment, De Kalb, IL 60115 USA
Titolo Testata:
JOURNAL OF CLASSIFICATION
fascicolo: 2, volume: 18, anno: 2001,
pagine: 185 - 207
SICI:
0176-4268(2001)18:2<185:CUAATB>2.0.ZU;2-4
Fonte:
ISI
Lingua:
ENG
Soggetto:
PROXIMITY MATRICES; ALGORITHM;
Keywords:
optimization; cluster analysis; ultrametric; additive tree; structural representation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Social & Behavioral Sciences
Citazioni:
21
Recensione:
Indirizzi per estratti:
Indirizzo: Smith, TJ No Illinois Univ, Dept Educ Technol Res & Assessment, De Kalb, IL 60115 USA No Illinois Univ De Kalb IL USA 60115 nt, De Kalb, IL 60115 USA
Citazione:
T.J. Smith, "Constructing ultrametric and additive trees based on the L-1 norm", J CLASSIF, 18(2), 2001, pp. 185-207

Abstract

A heuristic method for identifying and fitting ultrametric and additive trees is presented, based on iteratively re-weighted iterative projection (IRIP) that minimizes a least absolute deviations (L-1) criterion. Examples ofultrametric and additive trees fitted to two extant data sets are given, plus a Monte Carlo analysis to assess the impact of both typical data error and extreme values on fitted trees. Solutions are compared to the least-squares (L-2) approach of Hubert and Arabie (1995a), with results indicating that (with these data) the L-1 and L-2 optimization strategies perform very similarly. A number of observations are made concerning possible uses of anL-1 approach, the nature and number of identified locally optimal solutions, and metric recovery differences between ultrametrics and additive trees.

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Documento generato il 25/01/20 alle ore 00:16:24