Self-Organizing Maps

Identify prototype vectors for clusters of examples, example distributions, and similarity relationships between clusters

Apps

Solve clustering problem using self-organizing map (SOM) networks

`nctool`	Open Neural Net Clustering app
`view`	View shallow neural network

selforgmap Self-organizing map

`train`	Train shallow neural network
`plotsomhits`	Plot self-organizing map sample hits
`plotsomnc`	Plot self-organizing map neighbor connections
`plotsomnd`	Plot self-organizing map neighbor distances
`plotsomplanes`	Plot self-organizing map weight planes
`plotsompos`	Plot self-organizing map weight positions
`plotsomtop`	Plot self-organizing map topology
`genFunction`	Generate MATLAB function for simulating shallow neural network

Cluster Data with a Self-Organizing Map
Group data by similarity using the Neural Net Clustering app or command-line functions.
Deploy Shallow Neural Network Functions
Simulate and deploy trained shallow neural networks using MATLAB^® tools.
Deploy Training of Shallow Neural Networks
Learn how to deploy training of shallow neural networks.
Iris Clustering
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.
Gene Expression Analysis
This example demonstrates looking for patterns in gene expression profiles in baker's yeast using neural networks.
One-Dimensional Self-Organizing Map
Neurons in a 2-D layer learn to represent different regions of the input space where input vectors occur.
Two-Dimensional Self-Organizing Map
As in one-dimensional problems, this self-organizing map will learn to represent different regions of the input space where input vectors occur.

Cluster with Self-Organizing Map Neural Network
Use self-organizing feature maps (SOFM) to classify input vectors according to how they are grouped in the input space.