Last updated: Jan 17, 2024
The 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.
Example
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)
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. |