The b-chromatic number of a graph
Discrete Applied Mathematics
Semantic Clustering of Index Terms
Journal of the ACM (JACM)
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
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
McSOM: Minimal Coloring of Self-Organizing Map
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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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.