By Topic

A new tree-structured self-organizing map for data analysis

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)
Costa, J.A.F. ; Dept. of Comput. Eng., Univ. Estadual de Campinas, Sao Paulo, Brazil ; de Andrade Netto, M.L.

This paper presents a new algorithm for dynamical generation of a hierarchical structure of self-organizing maps (SOM) with applications to data analysis. Different from other tree-structured SOM approaches, in this case the tree nodes are actually maps. From top to down, maps are automatically segmented by using the U-matrix information, which presents relations between neighboring neurons. The automatic map partitioning algorithm is based on mathematical morphology segmentation and it is applied to each map in each level of the hierarchy. Clusters of neurons are automatically identified and labeled and generate new sub-maps. Data are partitioned accordingly the label of its best match unit in each level of the tree. The algorithm may be seen as a recursive partition clustering method with multiple prototypes cluster representation, which enables the discoveries of clusters in a variety of geometrical shapes

Published in:

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on  (Volume:3 )

Date of Conference: