Last updated: Jan 18, 2024
The TwoStep node uses a two-step clustering method. The first step makes a
single pass through the data to compress the raw input data into a manageable set of subclusters.
The second step uses a hierarchical clustering method to progressively merge the subclusters into
larger and larger clusters. TwoStep has the advantage of automatically estimating the optimal number
of clusters for the training data. It can handle mixed field types and large data sets
efficiently.
Example
node = stream.create("twostep", "My node")
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("inputs", ["Age", "K", "Na", "BP"])
node.setPropertyValue("partition", "Test")
node.setPropertyValue("use_model_name", False)
node.setPropertyValue("model_name", "TwoStep_Drug")
node.setPropertyValue("use_partitioned_data", True)
node.setPropertyValue("exclude_outliers", True)
node.setPropertyValue("cluster_label", "String")
node.setPropertyValue("label_prefix", "TwoStep_")
node.setPropertyValue("cluster_num_auto", False)
node.setPropertyValue("max_num_clusters", 9)
node.setPropertyValue("min_num_clusters", 3)
node.setPropertyValue("num_clusters", 7)
twostepnode Properties |
Values | Property description |
|---|---|---|
inputs
|
[field1 ... fieldN] | TwoStep models use a list of input fields, but no target. Weight and frequency fields are not recognized. See Common modeling node properties for more information. |
standardize
|
flag | |
exclude_outliers
|
flag | |
percentage
|
number | |
cluster_num_auto
|
flag | |
min_num_clusters
|
number | |
max_num_clusters
|
number | |
num_clusters
|
number | |
cluster_label
|
String
Number
|
|
label_prefix
|
string | |
distance_measure
|
Euclidean
Loglikelihood
|
|
clustering_criterion
|
AIC
BIC
|