Same-size k-Means

This component identifies equally-sized clusters of homogeneous items in a dataset. First, K-means algorithm is used to derive centers of the clusters. Then, every point is associated in turn to the closest cluster, in a way that forces each cluster to be of equal size. The size of each cluster will approximately be the number of original rows divided by the number of clusters.

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.