Document Detail


Growing self-organizing trees for autonomous hierarchical clustering.
MedLine Citation:
PMID:  23041056     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
This paper presents a new unsupervised learning method based on growing processes and autonomous self-assembly rules. This method, called Growing Self-organizing Trees (GSoT), can grow both network size and tree topology to represent the topological and hierarchical dataset organization, allowing a rapid and interactive visualization. Tree construction rules draw inspiration from elusive properties of biological organization to build hierarchical structures. Experiments conducted on real datasets demonstrate good GSoT performance and provide visual results that are generated during the training process.
Authors:
Nhat-Quang Doan; Hanane Azzag; Mustapha Lebbah
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-9-19
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  -     ISSN:  1879-2782     ISO Abbreviation:  Neural Netw     Publication Date:  2012 Sep 
Date Detail:
Created Date:  2012-10-8     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012 Elsevier Ltd. All rights reserved.
Affiliation:
Université Paris 13, Sorbonne Paris Cité, Laboratoire d'Informatique de Paris-Nord (LIPN), CNRS (UMR 7030), 99, av. J-B Clement, F-93430 Villetaneuse, France. Electronic address: nhat-quang.doan@lipn.univ-paris13.fr.
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