An Information-Theoretic-Cluster Visualization for Self-Organizing Maps
نویسندگان
چکیده
منابع مشابه
Self-organizing maps with information theoretic learning
The self organizing map (SOM) is one of the popular clustering and data visualization algorithms and has evolved as a useful tool in pattern recognition, data mining since it was first introduced by Kohonen. However, it is observed that the magnification factor for such mappings deviates from the information-theoretically optimal value of 1 (for the SOM it is 2/3). This can be attributed to the...
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The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects highdimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. Despite the popular use of the algorithm for clustering and information visualisation, a system has been lacking that combines the fast ...
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We present two enhanced visualization techniques for the self-organizing map allowing the intuitive representation of input data similarity. The general idea of both approaches is to visualize the relationship of nodes to facilitate the detection of cluster boundaries without modifying the architecture or the basic training process of SOM. One approach mirrors the movement of weight vectors dur...
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Visualization of Wikipedia categories using Self Organizing Maps shows an overview of categories and their relations, helping to narrow down search domains. Selecting particular neurons this approach enables retrieval of conceptually similar categories. Evaluation of neural activations indicates that they form coherent patterns that may be useful for building user interfaces for navigation over...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2018
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2017.2699674