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Use of Minimal Spanning Trees on Self-Organizing Maps
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 Title & Authors
Use of Minimal Spanning Trees on Self-Organizing Maps
Jang, Yoo-Jin; Huh, Myung-Hoe; Park, Mi-Ra;
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As one of the unsupervised learning neural network methods, self-organizing maps(SOM) are applied to various fields. It reduces the dimension of multidimensional data by representing observations on the low dimensional manifold. On the other hand, the minimal spanning tree(MST) of a graph that achieves the most economic subset of edges connecting all components by a single open loop. In this study, we apply the MST technique to SOM with subnodes. We propose SOM`s with embedded MST and a distance measure for optimum choice of the size and shape of the map. We demonstrate the method with Fisher`s Iris data and a real gene expression data. Simulated data sets are also analyzed to check the validity of the proposed method.
Self-organizing map(SOM);minimal spanning tree(MST);data visualization;distance measure;
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