IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
STORM: a novel information fusion and cluster interpretation technique
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Graph based representations of density distribution and distances for self-organizing maps
IEEE Transactions on Neural Networks
Clustering and visualizing SOM results
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Gravitational clustering of the self-organizing map
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Spectral clustering as an automated SOM segmentation tool
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
A new approach for data clustering and visualization using self-organizing maps
Expert Systems with Applications: An International Journal
SOMM – self-organized manifold mapping
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Patient's motion recognition based on SOM-decision tree
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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Kohonen's self-organizing map (SOM) is a popular neural network architecture for solving problems in the field of explorative data analysis, clustering, and data visualization. One of the major drawbacks of the SOM algorithm is the difficulty for nonexpert users to interpret the information contained in a trained SOM. In this paper, this problem is addressed by introducing an enhanced version of the Clusot algorithm. This algorithm consists of two main steps: 1) the computation of the Clusot surface utilizing the information contained in a trained SOM and 2) the automatic detection of clusters in this surface. In the Clusot surface, clusters present in the underlying SOM are indicated by the local maxima of the surface. For SOMs with 2-D topology, the Clusot surface can, therefore, be considered as a convenient visualization technique. Yet, the presented approach is not restricted to a certain type of 2-D SOM topology and it is also applicable for SOMs having an n-dimensional grid topology.