title: Growing self-organizing trees for autonomous hierarchical clustering.
abstract: 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.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. nhat-quang.doan@lipn.univ-paris13.fr
authors.forename: Nhat-Quang
authors.initials: NQ
authors.lastname: Doan
authors.forename: Hanane
authors.initials: H
authors.lastname: Azzag
authors.forename: Mustapha
authors.initials: M
authors.lastname: Lebbah
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003663:Decision Trees; D008962:Models, Theoretical; D016571:Neural Networks, Computer