Database Reference
In-Depth Information
Table 2.16 Unsupervised modeling techniques.
Clustering techniques
Data reduction techniques
•K-means
• TwoStep cluster
• Kohonen network/self-organizing map
• Principal components analysis
• Factor analysis
Clustering models automatically detect natural groupings of records. They can
be used to segment customers. All customers are assigned to one of the derived
clusters according to their input data patterns and their profiles. Although an
explorative technique, clustering also requires the evaluation of the derived clusters
before selecting a final solution. The revealed clusters should be understandable,
meaningful, and actionable in order to support the development of an effective
segmentation scheme.
Association models identify events/products/attributes that tend to co-occur.
They can be used for market basket analysis and in all other ''affinity'' business
problems related to questions such as ''what goes with what?'' They generate IF
...
THEN rules which associate antecedents to a specific consequent. Sequence
models are an extension of association models that also take into account the order
of events. They detect sequences of events and can be used in web path analysis
and in any other ''sequence'' type of problem.
Table 2.16 lists unsupervised modeling techniques in the fields of clustering
and data reduction. Once again the table is not meant to be exhaustive but rather
an indicative listing of some of the most popular algorithms.
One last thing to note about dataminingmodels: they should not be viewed as a
stand-alone procedure but rather as one of the steps in a well-designed procedure.
Model results depend greatly on the preceding steps of the process (business
understanding, data understanding, and data preparation) and on decisions and
actions that precede the actual model training. Although most data mining models
automatically detect patterns, they also depend on the skills of the persons
involved. Technical skills are not enough. They should be complemented with
business expertise in order to yield meaningful instead of trivial or ambiguous
results. Finally, a model can only be considered as effective if its results, after being
evaluated as useful, are deployed and integrated into the organization's everyday
business operations.
Since the topic focuses on customer segmentation, a thorough presentation
of supervised algorithms is beyond its scope. In the next chapter we will introduce
only the key concepts of decision trees, as this is a technique that is often used
in the framework of a segmentation project for scoring and profiling. We will,
however, present in detail in that chapter those data reduction and clustering
techniques that are widely used in segmentation applications.
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