k-Means (distance)

This component runs K-means algorithm and outputs the Euclidean distance between every point and the clusters' centroids.

In the configuration dialog, you can select whether to calculate the distance to every cluster's centroid or only to the one each points belongs to.

The clustering algorithm uses the Euclidean distance on the selected attributes. The data is not normalized by the node (if required, you should consider to use the "Normalizer" as a preprocessing step).

This component is free to use and modify.
Author: Andrea De Mauro, aboutbigdata.net

Input ports

1. Type: Table
   Input
   Input data for the clustering. Only numeric columns are considered in the clustering.

Output ports

1. Type: Table
   Data with clusters
   The input data labeled with the cluster they are contained in.
2. Type: Table
   Clusters
   The created clusters, i.e. the coordinates of the centroids.
3. Type: PMML
   PMML Cluster Model
   PMML cluster model