Dynamic clustering for interval data based on \(L_2\) distance
type: Item
instance of: scholarly article
title: Dynamic clustering for interval data based on \(L_2\) distance
zbMATH Open document ID: 1114.62070
author: Paula Brito
author: Francisco de A. T. de Carvalho
author: H.-H. Bock
published in: Computational Statistics
publication date: 2007-05-29T00:00:00Z
review text: The authors consider a \(k\)-clustering dynamic algorithm for clusterization of multidimensional intervals. It is based on the Minkowski-like \(L_2\)-type distance. Three types of variables standardization are considered, they are based on the dispersion of interval centres, dispersion of interval boundaries and the global range. Heterogeneity measures based on sums of squares are discussed. Results of simulations and application to car data are presented.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62-07
zbMATH DE Number: 5158598
zbMATH Keywords: dynamic \(k\)-clustering
zbMATH Keywords: Minkowski distance
zbMATH Keywords: symbolic data
describes a project that uses: SODAS
MaRDI profile type: Publication
full work available at URL: s00180-006-0261-z
OpenAlex ID: W2007961939
cites work: Automatische Klassifikation
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cites work: Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
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DOI: 10.1007/S00180-006-0261-Z
description: scientific article; zbMATH DE number 5158598