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Evaluate Clustering Model VI

LabVIEW 2017 Analytics and Machine Learning Toolkit Help

Edition Date: July 2017

Part Number: 377059A-01

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Owning Palette: Clustering VIs

Requires: Analytics and Machine Learning Toolkit

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

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.

0Davies 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.
1Dunn 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.
2Jaccard 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.
3Rand 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.
4AIC—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.
5BIC—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.

 

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