Evaluate Clustering Model VI

Owning Palette: Clustering VIs

Requires: Analytics and Machine Learning Toolkit

Evaluates a trained clustering model with training data or new test data.

Example

	model in specifies the information about the entire workflow of the model.
	predicted labels specifies the predicted labels of training data or test data.
	evaluation metric specifies the criterion to evaluate the trained clustering model. 0 Davies Bouldin Index—Evaluates the clustering model using the Davies Bouldin Index metric. The lower the value of metric, the better the compactness and separation of the clustering model. If you select this metric, you do not need to specify predicted labels. 1 Dunn Index (default)—Evaluates the clustering model using the Dunn Index metric. The higher the value of metric, the better the compactness and separation of the clustering model. If you select this metric, you do not need to specify predicted labels. 2 Jaccard Index—Evaluates the clustering model using the Jaccard Index metric. The lower the value of metric, the better the separation of the clustering model. If you select this metric, you must specify predicted labels. 3 Rand Index—Evaluates the clustering model using the Rand Index metric. The lower the value of metric, the better the separation of the clustering model. If you select this metric, you must specify predicted labels. 4 AIC—Evaluates the GMM model using the Akaike Information Criterion (AIC) metric. The lower the value of metric, the better the GMM model is. If you select this metric, you do not need to specify predicted labels. This metric applies to the GMM model only. 5 BIC—Evaluates the GMM model using the Bayesian Information Criterion (BIC) metric. The lower the value of metric, the better the GMM model is. If you select this metric, you do not need to specify predicted labels. This metric applies to the GMM model only.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	model out returns the information about the entire workflow of the model. Wire model out to the reference input of a standard Property Node to get an AML Analytics Property Node.
	metric returns the metric you selected in evaluation metric.
	error out contains error information. This output provides standard error out functionality.

Example

Refer to the Clustering (Set Parameters, Training) VI in the labview\examples\AML\Clustering directory for an example of using the Evaluate Clustering Model VI.