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The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm.
The feature prototypes are input to the Self Organizing Map (SOM).
Figure 4 Self organizing map of the chemical space of compounds considered colored by properties.
The Data Mining Tool was used for cluster analysis with the Self Organizing Map (SOM) algorithm.
Samples were arranged by time post-infection and genes clustered using a Self Organizing Map (SOM) algorithm [ 23].
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self-organizing map.
The inverse kinematic relationship of the manipulator is learned using a Kohonen's self-organizing map.
To overcome these problems, an extended self-organizing map (ESOM) was recently developed.
The Kohonen Self-Organizing Map (KSOM) estimator is prescribed as a useful tool for infilling the missing data in hydrometeorology.
The learning process for the Self-Organizing Map was computed with the Self-Organizing Map Toolbox developed by the Laboratory of Information and Computer Science, Helsinki University of Technology, in MATLAB environments [ 34].
3 Most clustering algorithms are distance-based whereas some involve hierarchical clustering, K-means clustering, or a self-organizing map.
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