Visualization using multi-layered U-Matrix in growing Tree-Structured self-organizing feature map
Takashi Yamaguchi, Takumi Ichimura · 2011
Self-organizing feature map (SOM) is well known artificial neural network using unsupervised learning for the data visualization and vector quantization. SOM has been used for cluster analysis. On the other hand, SOM cannot produce clarified clusters. And so SOM clustering capability is depends on visualization method. We proposed a variant of SOM that construct hierarchical neural network structure to clarify cluster boundaries in previous research. In this paper, we proposed a visualization method for this growing Tree-Structured SOM and discuss the computational result of Iris data.