NDLI: Different Aspects of Clustering The Self-Organizing Maps

Content Provider	Springer Nature Link
Author	Elghazel, Haytham Benabdeslem, Khalid
Copyright Year	2013
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.
Starting Page	97
Ending Page	114
Page Count	18
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	39
Issue Number	1
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2013-02-28
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	SOM K-means AHC Graph coloring Topological organization Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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