twostepnode properties

Last updated: Feb 12, 2025

Twostep node icon 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)

Table 1. twostepnode properties
`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`

Was the topic helpful?

0/1000

ExampleCopy link to section

Example