Validating visual clusters in large datasets: fixed point clusters of spectral features.
type: Item
instance of: scholarly article
title: Validating visual clusters in large datasets: fixed point clusters of spectral features.
zbMATH Open document ID: 1043.68089
DOI: 10.1016/S0167-9473(02)00077-4
author: Christian Hennig
author: Norbert Christlieb
published in: Computational Statistics and Data Analysis
publication date: 2003-01-21T00:00:00Z
review text: Finding clusters in large datasets is a difficult task. Almost all computationally feasible methods are related to \(k\)-means and need a clear partition structure of the data, while most such datasets contain masking outliers and other deviations from the usual models of partitioning cluster analysis. It is possible to look for clusters informally using graphic tools like the grand tour, but the meaning and the validity of such patterns is unclear. In this paper, a three-step-approach is suggested: In the first step, data visualization methods like the grand tour are used to find cluster candidate subsets of the data. In the second step, reproducible clusters are generated from them by means of fixed point clustering, a method to find a single cluster at a time based on the Mahalanobis distance. In the third step, the validity of the clusters is assessed by the use of classification plots. The approach is applied to an astronomical dataset of spectra from the Hamburg/ESO survey.
Mathematics Subject Classification ID: 68T10
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68U99
zbMATH DE Number: 1856185
zbMATH Keywords: Discriminant coordinates
zbMATH Keywords: Outliers
zbMATH Keywords: Contamination model
zbMATH Keywords: Sky surveys
zbMATH Keywords: Stellar populations
zbMATH Keywords: Data visualization
zbMATH Keywords: Classification plots
Wikidata QID: Q60471688
describes a project that uses: XGobi
describes a project that uses: clusfind
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1856185