Self-Organizing Maps

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

Apps

Neural Net ClusteringCluster data by training a self-organizing maps network

Functions

nnstartNeural network getting started GUI
viewView neural network
selforgmapSelf-organizing map
trainTrain shallow neural network
plotsomhitsPlot self-organizing map sample hits
plotsomncPlot self-organizing map neighbor connections
plotsomndPlot self-organizing map neighbor distances
plotsomplanesPlot self-organizing map weight planes
plotsomposPlot self-organizing map weight positions
plotsomtopPlot self-organizing map topology
genFunctionGenerate MATLAB function for simulating shallow neural network

Examples and How To

Cluster Data with a Self-Organizing Map

Group data by similarity using the Neural Network 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.

Concepts

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.