Clustering Outputs
The following outputs can be generated by the Clustering tool. Depending on your configuration and analysis requirements, you can select one or more outputs from the Outputs section.
Ouptut | Type | Description |
|---|---|---|
Clusters | Condition | A condition with capsules representing the cluster assigned to each observation. Each capsule includes a Cluster property with a value of "Cluster X", where X corresponds to the cluster number identified by the model. This output can be used to visualize and analyze how observations are grouped over time. |
Cluster Centers | Scalar | Shows the center point of each cluster learned during training. Cluster centers represent the typical characteristics of observations within each cluster. |
Inertia | Scalar | Measures how closely observations are grouped around their assigned cluster centers. Lower values generally indicate more compact and well-defined clusters. |
Init | Scalar | Indicates the initialization method used to select the starting cluster centers before training. This information can be useful when reviewing or reproducing model results. |
K-Means Algorithm | Scalar | Indicates the algorithm variant used to train the clustering model. |
Model input properties | Scalar | Provides information about the model inputs used during training. This output can be used for model validation and troubleshooting. |
Training window | Condition | Identifies the time range used to train the Clustering model. This output can be used to verify the training period and compare it with scored data. |