Cartogram-based Data Visualization using the Growing Hierarchical SOM

被引：0

作者：

Martin, Angela ^{[1
]}

Vellido, Alfredo ^{[1
]}

机构：

[1] Univ Politecn Cataluna, Dept Llenguatges & Sistemes Informat, ES-08034 Barcelona, Spain

来源：

ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE OF THE CATALAN ASSOCIATION FOR ARTIFICIAL INTELLIGENCE | 2013年 / 256卷

关键词：

GHSOM; visualization; hierarchical clustering; unsupervised learning;

D O I：

10.3233/978-1-61499-320-9-249

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Model interpretability is a problem of knowledge extraction from the patterns found in data to which data visualization can contribute. Nonlinear dimensionality reduction techniques provide flexible visual insight, but their locally varying representation distortion makes interpretation far from intuitive. In this paper, we apply a cartogram method, based on techniques of geographic representation, to data visualization. It allows reintroducing this distortion, measured as a U-matrix, in the visual maps of the Growing Hierarchical Self Organising Map (GHSOM).

引用

页码：249 / 252

页数：4

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