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
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;
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
DOI :
10.1109/IJCNN.2012.6252396
