Data Mining Techniques for Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 98

Figure 3.12 IBM SPSS Modeler recommended Kohonen Expert options.

• Maximum (Euclidean) distance from the cluster center. Another statistic that
summarizes the degree of concentration of each cluster is the maximum distance
from the cluster center, the cluster radius. In a way it represents the range of
each cluster since it denotes how far apart the remotest member of the
cluster lies.
• Evaluation of the distance between the members of a cluster and their cluster
centroid. Analysts should average these distances over all members of a cluster
and look for clusters with disproportionately large, average distances. These
clusters are candidate for further segmentation. A technical cluster cohesion
measure which is based on the (squared Euclidean) distances between the data
points and their centroid is the sum of squares error (SSE). In order to compare
models we can use the average SSE calculated as follows:

N
i

1

dist ( c i , x ) 2 Average SSE =

∈ C x ∈ C i

where c i is the centroid of cluster i , x a data point or record of cluster i , and N
the total cases. A solution with smaller SSE is preferred.