kmeansasnode properties

K-Means is one of the most commonly used clustering algorithms. It clusters data points into a predefined number of clusters. The K-Means-AS node in SPSS® Modeler is implemented in Spark. For details about K-Means algorithms, see https://spark.apache.org/docs/3.4.0/ml-clustering.html#clustering. Note that the K-Means-AS node performs one-hot encoding automatically for categorical variables.

Table 1. kmeansasnode properties

kmeansasnode Properties	Values	Property description
roleUse	string	Specify predefined to use predefined roles, or custom to use custom field assignments. Default is predefined.
autoModel	Boolean	Specify true to use the default name ($S-prediction) for the new generated scoring field, or false to use a custom name. Default is true.
features	field	List of the field names for input when the roleUse property is set to custom.
name	string	The name of the new generated scoring field when the autoModel property is set to false.
clustersNum	integer	The number of clusters to create. Default is 5.
initMode	string	The initialization algorithm. Possible values are k-means|| or random. Default is k-means||.
initSteps	integer	The number of initialization steps when initMode is set to k-means||. Default is 2.
advancedSettings	Boolean	Specify true to make the following four properties available. Default is false.
maxIteration	integer	Maximum number of iterations for clustering. Default is 20.
tolerance	string	The tolerance to stop the iterations. Possible settings are 1.0E-1, 1.0E-2, ..., 1.0E-6. Default is 1.0E-4.
setSeed	Boolean	Specify true to use a custom random seed. Default is false.
randomSeed	integer	The custom random seed when the setSeed property is true.