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Clojure for Machine Learning by Akhil Wali

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Using SOMs

As we mentioned earlier in Chapter 4, Building Neural Networks, SOMs can be used to model unsupervised machine learning problems such as clustering (for more information, refer to Self-organizing Maps as Substitutes for K-Means Clustering). To quickly recap, an SOM is a type of ANN that maps input values with a high number of dimensions to a low-dimensional output space. This mapping preserves patterns and topological relations between the input values. The neurons in the output space of an SOM will have higher activation values for input values that are spatially close to each other. Thus, SOMs are a good solution for clustering input data with a large number of dimensions.

The Incanter library provides a concise SOM implementation ...

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