Abstract
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.
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Elghazel, H., Benabdeslem, K., Kheddouci, H. (2009). McSOM: Minimal Coloring of Self-Organizing Map. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_15
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DOI: https://doi.org/10.1007/978-3-642-03348-3_15
Publisher Name: Springer, Berlin, Heidelberg
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