Database Reference
In-Depth Information
Table 2.8 The cluster membership field.
Input fields
Model-generated field
Customer
Average monthly
Average monthly
Cluster membership
ID
number of SMS calls number of voice calls
field
1
27
144
Cluster 1 - heavy
voice users
2
32
44
Cluster 2 - typical
users
3
41
30
Cluster 2 - typical
users
4
125
21
Cluster 2 - SMS users
5
105
23
Cluster 2 - SMS users
6
20
121
Cluster 1 - heavy
voice users
in the cluster formation. Each clustering solution should be thoroughly examined
and the profiles of the clusters outlined. This is usually accomplished by simple
reporting techniques, but it can also include the application of supervised mod-
eling techniques such as classification techniques, aiming to reveal the distinct
characteristics associated with each cluster.
This profiling phase is an essential step in the clustering procedure. It
can provide insight on the derived segmentation scheme and it can also help
in the evaluation of the scheme's usefulness. The derived clusters should be
evaluated with respect to the business objective they were built to serve. The
results should make sense from a business point of view and should generate
business opportunities. The marketers and data miners involved should try to
evaluate different solutions before selecting the one that best addresses the
original business goal.
Available clustering models include the following:
Agglomerative or hierarchical: Although quite outdated nowadays, we
present this algorithm since in a way it is the ''mother'' of all clustering
models. It is called hierarchical or agglomerative because it starts with a solution
where each record comprises a cluster and gradually groups records up to the
point where all of them fall into one supercluster. In each step it calculates the
distances between all pairs of records and groups the most similar ones. A table
(agglomeration schedule) or a graph (dendrogram) summarizes the grouping
steps and the respective distances. The analyst should consult this information,
identify the point where the algorithm starts to group disjoint cases, and then
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