Train Clustering Model
This article describes a component in Azure Machine Learning designer.
Use this component to train a clustering model.
The component takes an untrained clustering model that you have already configured using the K-Means Clustering component, and trains the model using a labeled or unlabeled data set. The component 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 component, which is the generic component 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 component to your pipeline in the designer. You can find the component under Machine Learning components, in the Train category.
Add the K-Means Clustering component, or another custom component 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 component 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 component. Select the Register dataset icon to save the model as a reusable component.
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 component in the inference pipeline.
See the set of components available to Azure Machine Learning.