Clustering of the self-organizing map

2000/05/01 by J. Vesanto, Juha Vesanto, E. Alhoniemi +1 · 11 citations
Computer Science · #Advanced Clustering Algorithms Research #Face and Expression Recognition #Neural Networks and Applications

paper · doi:10.1109/72.846731

openalex publication_date 2000/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

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.

Citations

• Nonmetric Multidimensional Scaling: A Numerical Method
• Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis

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