The b-chromatic number of a graph
Discrete Applied Mathematics
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
A new clustering approach for symbolic data and its validation: application to the healthcare data
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Different Aspects of Clustering The Self-Organizing Maps
Neural Processing Letters
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The Self-Organizing Map (SOM) is one of the most popular neural network methods. It is a powerful tool in visualization and analysis of high-dimensional data in various application domains such as Web analysis, information retrieval, and many other domains. The SOM maps the data on a low-dimensional grid which is generally followed by a clustering step of referent vectors (neurons or units). Different clustering approaches of SOM are considered in the literature. In particular, the use of hierarchical clustering and traditional k-means clustering are investigated. However, these approaches don't consider the topological organization provided by SOM. In this paper, we propose BcSOM, an extension of a recently proposed graph b-coloring clustering approach for clustering self organized map. It exhibits more important clustering features and enables to build a fine partition of referents by incorporating the neighborhood relations provided by SOM. The proposed approach is evaluated against benchmark data sets and its effectiveness is confirmed.