A similarity measure for chemical data: Applications to cluster analysis
István Kolossváry, Wolfhard Wegscheider · Journal of Chemometrics · 1990
Abstract The results of unsupervised pattern recognition methods are critically dependent on the measure of similarity used for clustering objects. There is little a priori information available on the relative utility of various similarity measures. We introduce here an alternative similarity measure based on the metric tensor measure (MTM). Two standard clustering strategies are tested with the proposed similarity measure: hierarchical clustering and the K‐median method. As data we use the ARCH obsidian data, a data set on Hungarian coal, and trace element data on Hungarian paprika. Differences from the Mahalanobis distance measure are described for intraclass relations.