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http://dx.doi.org/10.5351/KJAS.2005.18.1.083

Enhancing Visualization in Self-Organizing Maps  

Um Ick-Hyun (GDS Korea Inc., SinChang B/D)
Huh Myung-Hoe (Dept. of Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.18, no.1, 2005 , pp. 83-98 More about this Journal
Abstract
Exploring distributional patterns of multivariate data is very essential in understanding the characteristics of given data set, as well as in building plausible models for the data. For that purpose, low-dimensional visualization methods have been developed by many researchers along various directions. As one of methods, Kohonen's SOM (Self-Organizing Map) is prominent. SOM compresses the volume of the data, yields abstraction from the data and offers visual display on low-dimensional grids. Although it is proven quite effective, it has one undesirable property: SOM's display is discrete. In this study, we propose two techniques for enhancing quality of SOM's display, so that SOM's display becomes continuous. The proposed methods are demonstrated in two numerical examples.
Keywords
Kohonen's self-organizing map (SOM); Unsupervised learning; Visualization; IL-SOM; Subnode (k) SOM;
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