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).
Algorithm
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.
Usage
- 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
Notes
- Works only with numerical data
- Previously added scatterplot gets reused if possible, otherwise a new one is open
See also:
Sample: