Self-Organizing Maps
Apps
| Neural Net Clustering | Solve clustering problem using self-organizing map (SOM) networks |
Functions
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 |
Examples and How To
- 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.
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.
