Cart (Loading....) | Create Account
Close category search window

Visualization using multi-layered U-Matrix in growing Tree-Structured self-organizing feature map

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yamaguchi, T. ; Dept. of Inf. Syst., Tokyo Univ. of Inf. Sci., Chiba, Japan ; Ichimura, T.

Self-organizing feature map (SOM) is well known artificial neural network using unsupervised learning for the data visualization and vector quantization. SOM has been used for cluster analysis. On the other hand, SOM cannot produce clarified clusters. And so SOM clustering capability is depends on visualization method. We proposed a variant of SOM that construct hierarchical neural network structure to clarify cluster boundaries in previous research. In this paper, we proposed a visualization method for this growing Tree-Structured SOM and discuss the computational result of Iris data.

Published in:

Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on

Date of Conference:

9-12 Oct. 2011

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.