• DocumentCode
    2769664
  • Title

    Growing Self-organizing Trees for knowledge discovery from data

  • Author

    Doan, Nhat-Quang ; Azzag, Hanane ; Lebbah, Mustapha

  • Author_Institution
    LIPN, Univ. of Paris 13, Villetaneuse, France
  • fYear
    2012
  • fDate
    10-15 June 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    In this paper, we propose a new unsupervised learning method based on growing neural gas and using self-assembly rules to build hierarchical structures. Our method named GSoT (Growing Self-organizing Trees) depicts data in topological and hierarchical organization. This makes GSoT a good tool for data clustering and knowledge discovery. Experiments conducted on real data sets demonstrate the good performance of GSoT.
  • Keywords
    data mining; pattern clustering; trees (mathematics); unsupervised learning; GSoT; data clustering; growing neural gas; growing self-organizing trees; knowledge discovery; self-assembly rules; unsupervised learning method; Clustering algorithms; Clustering methods; Network topology; Prototypes; Topology; Training; Vectors; Clustering; data visualization; growing neural gas; hierarchical tree; self-organizing model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2012 International Joint Conference on
  • Conference_Location
    Brisbane, QLD
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4673-1488-6
  • Electronic_ISBN
    2161-4393
  • Type

    conf

  • DOI
    10.1109/IJCNN.2012.6252396
  • Filename
    6252396
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2769664