New clustering methods for interval data
type: Item
instance of: scholarly article
title: New clustering methods for interval data
zbMATH Open document ID: 1114.62069
author: Marie Chavent
author: Yves Lechevallier
author: Rosanna Verde
author: Francisco de A. T. de Carvalho
published in: Computational Statistics
publication date: 2007-05-29T00:00:00Z
review text: Two new clustering methods are proposed for multivariate interval data. They are modifications of the dynamic \(k\)-clustering algorithm based on minimization of distances from elements of the cluster to its prototype (centre). The number \(k\) of clusters is fixed. A sum of Hausdorff distances between intervals is used in the first modification and a specific dissimilarity measure based on a discretization of the intervals is used in the second modification. These algorithms are applied to data on monthly minimal and maximal daily average temperatures in 60 meteorological stations in China.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62P12
Mathematics Subject Classification ID: 62-07
zbMATH DE Number: 5158597
zbMATH Keywords: symbolic data
zbMATH Keywords: multivariate interval data
zbMATH Keywords: dynamic k-clustering
zbMATH Keywords: Hausdorff distance
MaRDI profile type: Publication
full work available at URL: s00180-006-0260-0
OpenAlex ID: W2004925710
cites work: Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data
cites work: Q3355062
cites work: Q4445234
cites work: Histograms in symbolic data analysis
cites work: Q4516042
cites work: Q4096265
cites work: Q3869981
cites work: Computing the minimum Hausdorff distance between two point sets on a line under translation
cites work: Q4424542
DOI: 10.1007/S00180-006-0260-0
description: scientific article; zbMATH DE number 5158597