|Neural Net Clustering||Solve clustering problem using self-organizing map (SOM) networks|
|Train shallow neural network|
|Plot self-organizing map sample hits|
|Plot self-organizing map neighbor connections|
|Plot self-organizing map neighbor distances|
|Plot self-organizing map weight planes|
|Plot self-organizing map weight positions|
|Plot self-organizing map topology|
|Generate MATLAB function for simulating shallow neural network|
Group data by similarity using the Neural Network Clustering App or command-line functions.
Simulate and deploy trained shallow neural networks using MATLAB® tools.
Learn how to deploy training of shallow neural networks.
This example illustrates how a self-organizing map neural network can cluster iris flowers into classes topologically, providing insight into the types of flowers and a useful tool for further analysis.
This example demonstrates looking for patterns in gene expression profiles in baker's yeast using neural networks.
Neurons in a 2-D layer learn to represent different regions of the input space where input vectors occur.
As in one-dimensional problems, this self-organizing map will learn to represent different regions of the input space where input vectors occur.
Use self-organizing feature maps (SOFM) to classify input vectors according to how they are grouped in the input space.