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Cluster data

Clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).


Clustering algorithm is based on k-means clustering. It aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.


  • Open table
  • Run from menu: ML | Cluster | Cluster...
  • Select numerical feature columns that will be used for clustering
  • Select number of required clusters. Integer number 1..n
  • Set "Show scatter plot" to open scatter plot after clustering
  • Run clustering

Result will be concatenated to source table as column with name "Cluster" and contains index of cluster for each table row.

Usage examples

"Cluster" column can be used as "COLOR" parameter in scatter plot.


  • Works only with numerical data
  • New scatter plot will be opened only, if there are no opened ones.

See also: