Algorithms for clustering data
Algorithms for clustering data
Self-Organizing Maps
Analysing a Contingency Table with Kohonen Maps: A Factorial Correspondence Analysis
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Simultaneous Two-Level Clustering Algorithm for Automatic Model Selection
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
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Clustering is a very powerful tool for automatic detection of relevant sub-groups in unlabeled data sets. It can be sometime very interesting to be able to regroup and visualize the attributes used to describe the data, in addition to the clustering of these data. In this paper, we propose a coclustering algorithm based on the learning of a Self Organizing Map. The new algorithm will thus be able at the same time to map data and features in a low dimensional sub-space, allowing simple visualization, and to produce a clustering of both data and features. The resulting output is therefore very informative and easy to analyze.