Enhancing Visualization in Self-Organizing Maps

被引：0

作者：

Um, Ick-Hyun ^{[1
]}

Huh, Myung-Hoe ^{[2
]}

机构：

[1] GDS Korea Inc, SinChang B-D 1F,81-10, Seoul 135818, South Korea

[2] Korea Univ, Dept Stat, Seoul 136701, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2005年 / 18卷 / 01期

关键词：

Kohonen's self-organizing map (SOM); Unsupervised learning; Visualization; IL-SOM; Subnode (k) SOM;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：83 / 98

页数：16

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