Title :
The growing hierarchical self-organizing map
Author :
Dittenbach, Michael ; Merkl, Dieter ; Rauber, Andreas
Author_Institution :
Inst. fur Softwaretech., Tech. Univ. Wien, Austria
Abstract :
We present the growing hierarchical self-organizing map. This dynamically growing neural network model evolves into a hierarchical structure according to the requirements of the input data during an unsupervised training process. We demonstrate the benefits of this novel neural network model by organizing a real-world document collection according to their similarities
Keywords :
full-text databases; self-organising feature maps; unsupervised learning; dynamically growing neural network model; growing hierarchical self-organizing map; hierarchical structure; real-world document collection; unsupervised training process; Adaptive systems; Artificial neural networks; Data mining; Data visualization; Neural networks; Self organizing feature maps;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859366
