Divisive methods

Description

The process shows, using the Maximum Variance (R15) dataset, how divisive hierarchical cluster analysis can be done.

Input

Maximum Variance (R15) [SIPU Datasets] [Maximum Variance]

The dataset contains 600 two-dimensional vectors, which form 15 separate groups. The task is to define the ideal cardinality of the groups, and to discover them.

Figure 11.13. The 600 two-dimensional vectors

The 600 two-dimensional vectors

Output

Figure 11.14. The subprocess

The subprocess


In order to perform divisive clustering, an arbitrary clustering method with which the division can be performed is necessary. In the initial state, all points belong to the same cluster, then, the method continuously divides the points into multiple groups, until finally, all points are placed into separate clusters. The operator determines the ideal number of clusters, and assigns the points to the clusters as well.

Figure 11.15. The report generated by the clustering

The report generated by the clustering


In the present case, the procedure determined the number of cluster as 63 groups.

Figure 11.16. The output of the analysis

The output of the analysis


Then the points are assigned to these groups.

Interpretation of the results

It can be seen that indeed, the method has created a larger number of clusters, but due to this, the central clusters can be separated better from each other.

Video

Workflow

clust_exp5.rmp

Keywords

Divisive method
divisive hierarchical clustering
cluster analysis

Operators

k-Means
Read CSV
Top Down Clustering