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The Kohonen self-organizing map method: An assessment
Authors:F Murtagh  M Hernández-Pajares
Institution:1. Space Telescope-European Coordinating Facility, European Southern Observatory, Karl-Schwarzschild-Str. 2, D-85748, Garching, Germany
2. Department de Matemàtica Aplicada i Telemàtica, Universitat Politècnica de Catalunya, Apartat 300002, E-08080, Barcelona, Spain
Abstract:The “self-organizing map” method, due to Kohonen, is a well-known neural network method. It is closely related to cluster analysis (partitioning) and other methods of data analysis. In this article, we explore some of these close relationships. A number of properties of the technique are discussed. Comparisons with various methods of data analysis (principal components analysis, k-means clustering, and others) are presented. This work has been partially supported for M. Hernández-Pajares by the DGCICIT of Spain under grant No. PB90-0478 and by a CESCA-1993 computer-time grant. Fionn Murtagh is affiliated to the Astrophysics Division, Space Science Department, European Space Agency.
Keywords:Partitioning  Optimization  Dimensionality reduction  Data display  Exploratory data analysis
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