kmeansnode properties

K-Means node iconThe K-Means node clusters the data set into distinct groups (or clusters). The method defines a fixed number of clusters, iteratively assigns records to clusters, and adjusts the cluster centers until further refinement can no longer improve the model. Instead of trying to predict an outcome, k-means uses a process known as unsupervised learning to uncover patterns in the set of input fields.


node = stream.create("kmeans", "My node")
# "Fields" tab
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("inputs", ["Cholesterol", "BP", "Drug", "Na", "K", "Age"])
# "Model" tab
node.setPropertyValue("use_model_name", True)
node.setPropertyValue("model_name", "Kmeans_allinputs")
node.setPropertyValue("num_clusters", 9)
node.setPropertyValue("gen_distance", True)
node.setPropertyValue("cluster_label", "Number")
node.setPropertyValue("label_prefix", "Kmeans_")
node.setPropertyValue("optimize", "Speed")
# "Expert" tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("stop_on", "Custom")
node.setPropertyValue("max_iterations", 10)
node.setPropertyValue("tolerance", 3.0)
node.setPropertyValue("encoding_value", 0.3)
Table 1. kmeansnode properties
kmeansnode Properties Values Property description
inputs [field1 ... fieldN] K-means models perform cluster analysis on a set of input fields but do not use a target field. Weight and frequency fields are not used. See Common modeling node properties for more information.
num_clusters number  
gen_distance flag  
cluster_label String Number  
label_prefix string  
mode Simple Expert  
stop_on Default Custom  
max_iterations number  
tolerance number  
encoding_value number  
optimize Speed Memory Specifies whether model building should be optimized for speed or for memory.