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New methods for self-organising map visual analysis

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Abstract

Self-organising maps (SOMs) have been used effectively in the visualisation and analysis of multidimensional data, with applications in exploratory data analysis (EDA) and data mining. We present three new techniques for performing visual analysis of SOMs. The first is a computationally light contraction method, closely related to the SOM’s training algorithm, designed to facilitate cluster and trajectory analysis. The second is an enhanced geometric interpolation method, related to multidimensional scaling, which forms a mapping from the input space onto the map. Finally, we propose the explicit representation of graphs like the SOM’s induced Delaunay triangulation for topology preservation and cluster analysis. The new methods provide an enhanced interpretation of the information contained in an SOM, leading to a better understanding of the data distributions with which they are trained, as well as providing insight into the map’s formation.

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Authors and Affiliations

  1. Facultad de Informática, Department of Architecture and Technology of Computer Systems, Campus de Montegancedo s/n, Boadilla del Monte, 28660, Madrid , Spain

    Manuel Rubio

  2. Facultad de Informática, Department of Applied Mathematics, Campus de Montegancedo s/n, Boadilla del Monte, 28660, Madrid , Spain

    Víctor Giménez

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  1. Manuel Rubio
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  2. Víctor Giménez
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Correspondence to Manuel Rubio.

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Rubio, M., Giménez, V. New methods for self-organising map visual analysis. Neural Comput & Applic 12, 142–152 (2003). https://doi.org/10.1007/s00521-003-0387-7

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