Clustering of the self-organizing map

TLDR

The self‑organizing map (SOM) is a powerful exploratory data‑mining tool that projects high‑dimensional data onto a low‑dimensional grid for visualization and analysis. This study evaluates various methods for clustering SOM units. The authors examine hierarchical agglomerative clustering and partitive k‑means clustering applied to SOM prototypes. The two‑stage approach—SOM followed by clustering—outperforms direct data clustering and reduces computation time.

Abstract

The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using k-means are investigated. The two-stage procedure--first using SOM to produce the prototypes that are then clustered in the second stage--is found to perform well when compared with direct clustering of the data and to reduce the computation time.