Module: Assign Data to Clusters
This article describes how to use the Assign Data to Clusters module in the Azure Machine Learning visual interface. The module generates predictions through a clustering model that was trained with the K-means clustering algorithm.
The Assign Data to Clusters module returns a dataset that contains the probable assignments for each new data point.
How to use Assign Data to Clusters
In the Azure Machine Learning visual interface, locate a previously trained clustering model. You can create and train a clustering model by using either of the following methods:
Configure the K-means clustering algorithm by using the K-Means Clustering module, and train the model by using a dataset and the Train Clustering Model module (this article).
You can also add an existing trained clustering model from the Saved Models group in your workspace.
Attach the trained model to the left input port of Assign Data to Clusters.
Attach a new dataset as input.
In this dataset, labels are optional. Generally, clustering is an unsupervised learning method. You are not expected to know the categories in advance. However, the input columns must be the same as the columns that were used in training the clustering model, or an error occurs.
To reduce the number of columns that are written to the interface from the cluster predictions, use Select columns in the dataset, and select a subset of the columns.
Leave the Check for Append or Uncheck for Result Only check box selected if you want the results to contain the full input dataset, including a column that displays the results (cluster assignments).
If you clear this check box, only the results are returned. This option might be useful when you create predictions as part of a web service.
Run the experiment.
- To view the values in the dataset, right-click the module, select Result datasets, and then select Visualize.