ACM Computing Surveys (CSUR)
Self-Organizing Maps
Multivariate Descriptive Statistical Analysis
Multivariate Descriptive Statistical Analysis
Introduction to Algorithms
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Clustering and its validation in a symbolic framework
Pattern Recognition Letters
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Simultaneous Two-Level Clustering Algorithm for Automatic Model Selection
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
McSOM: Minimal Coloring of Self-Organizing Map
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
From variable weighting to cluster characterization in topographic unsupervised learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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In this paper, a new graph based framework for clustering characterization is proposed. In this context, Self Organizing Map (SOM) is one popular method for clustering and visualizing high dimensional data, which is generally succeeded by another clustering methods (partitional or hierarchical) for optimizing the final partition. Recently, we have developed a new SOM clustering method based on graph coloring called McSOM. In the current study, we propose to automatically characterize the classes obtained by this method. To this end, we propose a new approach combining a statistical test with a maximum spanning tree for local features selection in each class. Experiments will be given over several databases for validating our approach.