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Putting Data Mining to Work 607
based on arithmetic operations. When data has many categorical variables,
then decision trees are quite useful, although association rules and link analysis
may be appropriate in some cases.
Number of Input Fields
In directed data mining applications, there should be a single target field or
dependent variable. The rest of the fields (except for those that are either
clearly irrelevant or clearly dependent on the target variable) are treated as
potential inputs to the model. Data mining methods vary in their ability to successfully
process large numbers of input fields. This can be a factor in deciding
on the right technique for a particular application.
In general, techniques that rely on adjusting a vector of weights that has an
element for each input field run into trouble when the number of fields grows
very large. Neural networks and memory-based reasoning share that trait.
Association rules run into a different problem. The technique looks at all possible
combinations of the inputs; as the number of inputs grows, processing
the combinations becomes impossible to do in a reasonable amount of time.
Decision-tree methods are much less hindered by large numbers of fields.
As the tree is built, the decision-tree algorithm identifies the single field that
contributes the most information at each node and bases the next segment of
the rule on that field alone. Dozens or hundreds of other fields can come along
for the ride, but won’t be represented in the final rules unless they contribute
to the solution.
TIP When faced with a large number of fields for a directed data mining
problem, it is a good idea to start by building a decision tree, even if the final
model is to be built using a different technique. The decision tree will identify a
good subset of the fields to use as input to a another technique that might be
swamped by the original set of input variables.
Free-Form Text
Most data mining techniques are incapable of directly handling free-form text.
But clearly, text fields often contain extremely valuable information. When
analyzing warranty claims submitted to an engine manufacturer by independent
dealers, the mechanic’s free-form notes explaining what went wrong and
what was done to fix the problem are at least as valuable as the fixed fields that
show the part numbers and hours of labor used.
One data mining technique that can deal with free text is memory-based
reasoning, one of the nearest neighbor methods discussed in Chapter 8. Recall
that memory-based reasoning is based on the ability to measure the distance