Train Clustering Model
This article describes a module in Azure Machine Learning designer.
Use this module to train a clustering model.
The module takes an untrained clustering model that you have already configured using the K-Means Clustering module, and trains the model using a labeled or unlabeled data set. The module creates both a trained model that you can use for prediction, and a set of cluster assignments for each case in the training data.
A clustering model cannot be trained using the Train Model module, which is the generic module for training machine learning models. That is because Train Model works only with supervised learning algorithms. K-means and other clustering algorithms allow unsupervised learning, meaning that the algorithm can learn from unlabeled data.
How to use Train Clustering Model
Add the Train Clustering Model module to your pipeline in the designer. You can find the module under Machine Learning Modules, in the Train category.
Add the K-Means Clustering module, or another custom module that creates a compatible clustering model, and set the parameters of the clustering model.
Attach a training dataset to the right-hand input of Train Clustering Model.
In Column Set, select the columns from the dataset to use in building clusters. Be sure to select columns that make good features: for example, avoid using IDs or other columns that have unique values, or columns that have all the same values.
If a label is available, you can either use it as a feature, or leave it out.
Select the option, Check for append or uncheck for result only, if you want to output the training data together with the new cluster label.
If you deselect this option, only the cluster assignments are output.
Submit the pipeline, or click the Train Clustering Model module and select Run Selected.
After training has completed:
To save a snapshot of the trained model, select the Outputs tab in the right panel of the Train model module. Select the Register dataset icon to save the model as a reusable module.
To generate scores from the model, use Assign Data to Clusters.
If you need to deploy the trained model in the designer, make sure that Assign Data to Clusters instead of Score Model is connected to the input of Web Service Output module in the inference pipeline.
See the set of modules available to Azure Machine Learning.