Growing self-organizing trees for autonomous hierarchical clustering

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...

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Veröffentlicht in:	Neural networks 2013-05, Vol.41, p.85-95
Hauptverfasser:	Doan, Nhat-Quang, Azzag, Hanane, Lebbah, Mustapha
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial Intelligence Cluster Analysis Clustering Data visualization Decision Trees Growing network Hierarchical tree Models, Theoretical Neural Networks (Computer) Self-organizing model
Online-Zugang:	Volltext
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