Choose this use-case if you wish to develop a model that groups unlabelled data into clusters. Clustering may be useful in customer segmentation, anomaly detection, and image segmentation. Given a tabular dataset containing the features you want to find clusters within, you can generate a model that locates and reports inherent patterns and anomalies within your data.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a Clustering model. Feature requirements include recommendations on additional datasets that might enhance model performance.
Training Models - Training Options and MetricsThis section describes all the available model training options that can be used to create a Clustering model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.
Prediction APIThis section discusses the prediction API method so that you could properly generate predictions from the model you deployed.