Set Clustering Model VI

LabVIEW 2018 Analytics and Machine Learning Toolkit Help

Edition Date: July 2018

Part Number: 377059B-01

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Owning Palette: Clustering VIs

Requires: Analytics and Machine Learning Toolkit

Sets properties for a trained clustering model before deployment.

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

K-Means

model in specifies the information about the entire workflow of the model.
K-Means model specifies the information about the trained K-Means model.
number of clusters specifies the number of clusters in the trained K-Means model.
centroids specifies the centroids of all clusters in the trained K-Means model. The number of rows must equal number of clusters and the number of columns must equal the number of features in the training data.
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.
error out contains error information. This output provides standard error out functionality.

GMM

model in specifies the information about the entire workflow of the model.
GMM model specifies the information about the trained GMM model.
number of clusters specifies the number of Gaussian distributions in the trained GMM model.
mean values specifies the mean values of all Gaussian distributions in the trained GMM model. The number of rows must equal number of clusters.
covariance values specifies the covariance values of all Gaussian distributions in the trained GMM model. The array size must equal number of clusters.
covariance matrix specifies the covariance matrix for each Gaussian distribution in the GMM model.
weights specifies the weights of all Gaussian distributions in the trained GMM model. The number of rows must equal number of clusters.
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.
error out contains error information. This output provides standard error out functionality.

DBSCAN

model in specifies the information about the entire workflow of the model.
DBSCAN model specifies the information about the trained DBSCAN model.
max distance specifies the maximum radius for a sample to form a neighborhood. The default is 1.
p specifies the power of the Minkowski metric that this VI uses to calculate distances between points. The default is 2.

The following equation defines the Minkowski metric:



where [x1, x2, …, xN] is the input feature vector and [core sample1, core sample2, …, core sampleN] is the core sample feature vector.
core samples specifies the core samples from the trained DBSCAN model.
core samples labels specifies the label of each core sample.
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.
error out contains error information. This output provides standard error out functionality.

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