Validating visual clusters in large datasets: fixed point clusters of spectral features.
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.
scientific article; zbMATH DE number 1856185