Train Clustering Model

This article describes a module of the visual interface (preview) for Azure Machine Learning service.

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.

Note

A clustering model cannnot 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

  1. Add the Train Clustering Model module to your experiment in Studio. You can find the module under Machine Learning Modules, in the Train category.

  2. Add the K-Means Clustering module, or another custom module that creates a compatible clustering model, and set the parameters of the clustering model.

  3. Attach a training dataset to the right-hand input of Train Clustering Model.

  4. 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.

  5. 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.

  6. Run the experiment, or click the Train Clustering Model module and select Run Selected.

Results

After training has completed:

  • To view the values in the dataset, right-click the module, select Result datasets, and click Visualize.

  • To save the trained model for later re-use, right-click the module, select Trained model, and click Save As Trained Model.

  • To generate scores from the model, use Assign Data to Clusters.

Next steps

See the set of modules available to Azure Machine Learning service.