Database Reference
In-Depth Information
Figure 6-6. The means for each attribute in our four (
k
) clusters.
We see in this view that cluster 0 has the highest average weight and cholesterol. With 0
representing Female and 1 representing Male, a mean of 0.591 indicates that we have more men
than women represented in this cluster. Knowing that high cholesterol and weight are two key
indicators of heart disease risk that policy holders can do something about, Sonia would likely want
to start with the members of cluster 0 when promoting her new programs. She could then extend
her programming to include the people in clusters 1 and 2, which have the next incrementally
lower means for these two key risk factor attributes. You should note that in this chapter's
example, the clusters' numeric order (0, 1, 2, 3) corresponds to decreasing means for each cluster.
This is coincidental. Sometimes, depending on your data set, cluster 0 might have the highest
means, but cluster 2 might have then next highest, so it's important to pay close attention to your
centroid values whenever you generate clusters.
So we know that cluster 0 is where Sonia will likely focus her early efforts, but how does she know
who to try to contact? Who are the members of this highest risk cluster? We can find this
information by selecting the Folder View radio button. Folder View is depicted in Figure 6-7.
Figure 6-7. Folder view showing the observations included in Cluster 0.
