Self-Organization in Parallel Coordinates

被引：0

作者：

Trutschl, Marjan ^{[1
]}

Kilgore, Phillip C. S. R. ^{[1
]}

Cvek, Urska ^{[1
]}

机构：

[1] LSU Shreveport, Dept Comp Sci, Shreveport, LA 71115 USA

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2013 | 2013年 / 8131卷

关键词：

Parallel Coordinates; Self-Organizing Map; Visualization; Multidimensional Data; VISUALIZATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Parallel coordinates has shown itself to be a powerful method of exploring and visualizing multidimensional data. However, applying this method to large datasets often introduces clutter, resulting in reduced insight of the data under investigation. We present a new technique that combines the classical parallel coordinates plot with a synthesized dimension that uses topological proximity as an indicator of similarity. We resolve the issue of over-plotting and increase the utility of the widely-used parallel coordinates visualization.

引用

页码：351 / 358

页数：8

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