Abstract
Self-organizing map (SOM) is an artificial neural network tool that is trained using unsupervised learning to produce a low dimensional representation of the input space, called a map. This map is generally the object of a clustering analysis step which aims to partition the referents vectors (map neurons) into compact and well-separated groups. In this paper, we consider the problem of the clustering SOM using different aspects: partitioning, hierarchical and graph coloring based techniques. Unlike the traditional clustering SOM techniques, which use k-means or hierarchical clustering, the graph-based approaches have the advantage of providing a partitioning of the SOM by simultaneously using dissimilarities and neighborhood relations provided by the map. We present the experimental results of several comparisons between these different ways of clustering.
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The diameter of one cluster is the largest dissimilarity between two objects belonging to the same cluster.
Heuristic proposed by T. Kohonen for automatically providing the number of neurons in the map.
The original direct b-coloring approach without any neighborhood information.
The original direct minimal coloring approach without any neighborhood information.
nrows is the number of SOM row’s.
Table 7 Runtime (in seconds) comparison of clustering SOM approaches
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Elghazel, H., Benabdeslem, K. Different Aspects of Clustering The Self-Organizing Maps. Neural Process Lett 39, 97–114 (2014). https://doi.org/10.1007/s11063-013-9292-y
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DOI: https://doi.org/10.1007/s11063-013-9292-y