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Neural Computing and Applications

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01.12.2003 | Original Article

New methods for self-organising map visual analysis

verfasst von: Manuel Rubio, Víctor Giménez

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2003

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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.

Vorheriger Artikel “Reasonable” support to knowledge sharing through schema analysis and articulation

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Titel: New methods for self-organising map visual analysis
verfasst von: Manuel Rubio
Víctor Giménez
Publikationsdatum: 01.12.2003
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2003
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-003-0387-7

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