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. © 2009 Springer.
CITATION STYLE
Elghazel, H., Benabdeslem, K., & Kheddouci, H. (2009). McSOM: Minimal coloring of self-organizing map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5678 LNAI, pp. 128–139). https://doi.org/10.1007/978-3-642-03348-3_15
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