Different aspects for clustering the self-organizing maps

Haytham Elghazel 1 Khalid Benabdeslem 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
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 self-organizing map 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 self-organizing map 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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Submitted on : Monday, April 11, 2016 - 4:28:19 PM
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  • HAL Id : hal-01301032, version 1

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Haytham Elghazel, Khalid Benabdeslem. Different aspects for clustering the self-organizing maps. Neural Processing Letters, Springer Verlag, 2014, 1, 39, pp.97-114. ⟨hal-01301032⟩

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