|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
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.
- 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.