Spherical Tree-Structured SOM and Its Application to Hierarchical Clustering - Koki Yoshioka; Hiroshi Dozono (Applied System Innovation, 2022)
When analyzing high-dimensional data with many elements, a data visualization that maps the data onto a low-dimensional space is often performed. By visualizing the data, humans can intuitively understand the structure of the data in the high-dimensional space. The self-organizing map (SOM) is one such data visualization method. We propose a spherical tree-structured SOM (S-TS-SOM), which speeds up the search for winner nodes and eliminates the unevenness of learning due to the position of the winner nodes by placing the nodes on a sphere and applying the tree search method. In this paper, we confirm that the S-TS-SOM can achieve the same results as a normal spherical SOM while reducing the learning time. In addition, we confirm the granularity of clustering on the tree structure of the S-TS-SOM.