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. |