Self Organizing Maps (DL_SOM)
This function creates self organizing maps used for data clustering
Self-organizing maps (SOM), also known as Kohonen maps, are a type of artificial neural network that can be used for unsupervised learning and data visualization. They are typically used for clustering and dimensionality reduction of complex data sets.
SOMs consist of a two-dimensional grid of nodes or neurons, each of which represents a different feature or attribute of the data. During training, the SOM learns to associate similar data points with adjacent neurons on the grid. This results in a topology-preserving mapping of the input space onto the two-dimensional grid.
SOMs are often used in data visualization applications because they can represent high-dimensional data in a two-dimensional map, making it easier to understand and interpret. They have been used in a variety of fields, including image and speech recognition, text mining, and pattern recognition.
Sample Request
Build an SOM for data clustering
Building a Self Organizing Map
SOM Construction
POST
https://autogon.ai/api/v1/engine/start
Request Body
Sample Request
Compile and train the pre-built SOM model, using passed in Hyper Parameters
Training a Sequential Organizing Map
SOM Training
POST
https://autogon.ai/api/v1
Request Body
Sample Request
Make predictions with the trained SOM model.
Predicting Cluster with an Self Organizing Map
SOM Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
Last updated