Self-organizing maps
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
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Information Processing and Management: an International Journal
Mining massive document collections by the WEBSOM method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Interactive Hierarchical SOM for Image Retrieval Visualization
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
On the impact of the metrics choice in SOM learning: some empirical results from financial data
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
Parallel batch training of the self-organizing map using openCL
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
An alternative map of the United States based on an n-dimensional model of geographic space
Journal of Visual Languages and Computing
Multistrategy self-organizing map learning for classification problems
Computational Intelligence and Neuroscience
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The two-dimensional (2D) Self-Organizing Map (SOM) has a well-known "border effect". Several spherical SOMs which use lattices of the tessellated icosahedron have been proposed to solve this problem. However, existing data structures for such SOMs are either not space efficient or are time consuming when searching the neighborhood. We introduce a 2D rectangular grid data structure to store the icosahedron-based geodesic dome. Vertices relationships are maintained by their positions in the data structure rather than by immediate neighbor pointers or an adjacency list. Increasing the number of neurons can be done efficiently because the overhead caused by pointer updates is reduced. Experiments show that the spherical SOM using our data structure, called a GeoSOM, runs with comparable speed to the conventional 2D SOM. The GeoSOM also reduces data distortion due to removal of the boundaries. Furthermore, we developed an interface to project the GeoSOM onto the 2D plane using a cartographic approach, which gives users a global view of the spherical data map. Users can change the center of the 2D data map interactively. In the end, we compare the GeoSOM to the other spherical SOMs by space complexity and time complexity.