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**Owning Palette:** Clustering VIs

**Requires:** Analytics and Machine Learning Toolkit

Initializes the hyperparameters of the density-based spatial clustering of applications with noise (DBSCAN) algorithm. You can either directly set the hyperparameters or specify multiple values for each hyperparameter. If you specify multiple values for each hyperparameter, the Train Clustering Model VI uses grid search to find the optimal set of hyperparameters.

Use the pull-down menu to select an instance of this VI.

hyperparameters specifies hyperparameters for the DBSCAN model.
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. | |||||||

untrained DBSCAN model returns the initialized DBSCAN model for training. | |||||||

error out contains error information. This output provides standard error out functionality. |

hyperparameter grids specifies multiple values for each hyperparameter of the DBSCAN model.
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hyperparameter optimization specifies the method of optimization to determine the optimal hyperparameter settings.
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evaluation metric specifies the criterion to evaluate the trained clustering model.
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. | |||||||||

untrained DBSCAN model returns the initialized DBSCAN model for training. | |||||||||

error out contains error information. This output provides standard error out functionality. |

Refer to the following VIs for examples of using the Initialize Clustering Model (DBSCAN) VI:

- Clustering (Set Parameters, Training) VI: labview\examples\AML\Clustering
- Clustering (Search Parameters, Training) VI: labview\examples\AML\Clustering

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