title: TreeSOM: Cluster analysis in the self-organizing map.
abstract: Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. The Kohonen self-organizing map (SOM) is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. Cluster analysis is often left to the user. In this paper we present the method TreeSOM and a set of tools to perform unsupervised SOM cluster analysis, determine cluster confidence and visualize the result as a tree facilitating comparison with existing hierarchical classifiers. We also introduce a distance measure for cluster trees that allows one to select a SOM with the most confident clusters.
authors.affiliation: Division of Medicinal Chemistry, Leiden/Amsterdam Center for Drug Research, Leiden University, Einsteinweg 55, 2333CC Leiden, The Netherlands. elena.samsonova@liacs.nl
authors.forename: Elena V
authors.initials: EV
authors.lastname: Samsonova
authors.forename: Joost N
authors.initials: JN
authors.lastname: Kok
authors.forename: Ad P
authors.initials: AP
authors.lastname: Ijzerman
mesh_terms: D000367:Age Factors; D000465:Algorithms; D000818:Animals; D002138:Calibration; D016000:Cluster Analysis; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D006801:Humans; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010802:Phylogeny; D011506:Proteins; D014197:Trees
chemical_list: D011506:Proteins