Smallest-last ordering and clustering and graph coloring algorithms
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
Self-Organizing-Map Based Clustering Using a Local Clustering Validity Index
Neural Processing Letters
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A graph based framework for clustering and characterization of SOM
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Different Aspects of Clustering The Self-Organizing Maps
Neural Processing Letters
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A 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 subject of a clustering analysis step which aims to partition the referents vectors (map neurons) in compact and well-separated groups. In this paper, we consider the problem of clustering self-organizing map using a modified graph minimal coloring algorithm. Unlike the traditional clustering SOM techniques, using k-means or hierarchical classification, our approach has the advantage to provide a partition of self-organizing map by simultaneously using dissimilarities and neighborhood relations provided by SOM. Experimental results on benchmark data sets demonstrate that the proposed clustering algorithm is able to cluster data in a better way than classical ones and indicates the effectiveness of SOM to offer real benefits for the original minimal coloring clustering approach.