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Cluster analysis represents another unsupervised multivariate technique suitable for the analysis of metabolomics data with self-organizing map (SOM), hierarchical cluster analysis (HCA) and k-means clustering being the most prominent representatives. In general, clustering methods group and visualize samples according to intrinsic similarities in their measurements, irrespective of sample groupings.
(Bartel., 2013) Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J.