Paper Info
Title
Visualization With Voronoi Tessellation And Moving Output Units In Self-Organizing Map Of The Real-Number System
Abstract
The Self-Organizing map (SOM) proposed by T. Kohonen is a method to produce a low-dimensional representation from high-dimensional input data automatically, where output units are restrictedly placed on grid points. We propose real-number SOM (RSOM), where output units are freely placed on the real-number coordinates plane and visualized as a Voronoi diagram. RSOM is a natural extension of the conventional SOM because Voronoi tessellation for the output units on the square grid generates square regions on the output plane, the same as the conventional SOM. We propose two methods of moving with preserving topology of the input data and several visualization method such as minimum spanning tree, variable boundary width and spherical RSOM. We illustrate moving methods decrease errors in results of simulation.
Year
DOI
Venue
2008
10.1109/IJCNN.2008.4634286
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8
Keywords
Field
DocType
measurement uncertainty,shape,data representation,voronoi diagram,artificial neural networks,data visualisation,grid generation,minimum spanning tree,mathematics,topology,quantization,data visualization,computational geometry,mesh generation,voronoi tessellation
Data visualization,Square tiling,Centroidal Voronoi tessellation,Pattern recognition,Visualization,Computer science,Self-organizing map,Artificial intelligence,Voronoi diagram,Grid,Machine learning,Minimum spanning tree
Conference
ISSN
Citations 
PageRank 
2161-4393
2
0.39
References 
Authors
4
3
Name
Order
Citations
PageRank
yuji matsumoto13008300.05
Motohide Umano218328.91
Masahiro Inuiguchi31430157.83