whether or not a fault existed, but to diagnose the kind of fault, given that one
was there. Thus there was no need to include fault-free cases in the training set.
The measured attributes were rather low level and had to be augmented by inter-
mediate concepts, that is, functions of basic attributes, which were defined in
consultation with the expert and embodied some causal domain knowledge.
The derived attributes were run through an induction algorithm to produce a
set of diagnostic rules. Initially, the expert was not satisfied with the rules
because he could not relate them to his own knowledge and experience. For
him, mere statistical evidence was not, by itself, an adequate explanation.
Further background knowledge had to be used before satisfactory rules were
generated. Although the resulting rules were quite complex, the expert liked
them because he could justify them in light of his mechanical knowledge. He
was pleased that a third of the rules coincided with ones he used himself and
was delighted to gain new insight from some of the others.
Performance tests indicated that the learned rules were slightly superior to
the handcrafted ones that had previously been elicited from the expert, and this
result was confirmed by subsequent use in the chemical factory. It is interesting
to note, however, that the system was put into use not because of its good per-
formance but because the domain expert approved of the rules that had been
learned.Marketing and sales
Some of the most active applications of data mining have been in the area of
marketing and sales. These are domains in which companies possess massive
volumes of precisely recorded data, data which—it has only recently been real-
ized—is potentially extremely valuable. In these applications, predictions them-
selves are the chief interest: the structure of how decisions are made is often
completely irrelevant.
We have already mentioned the problem of fickle customer loyalty and the
challenge of detecting customers who are likely to defect so that they can be
wooed back into the fold by giving them special treatment. Banks were early
adopters of data mining technology because of their successes in the use of
machine learning for credit assessment. Data mining is now being used to
reduce customer attrition by detecting changes in individual banking patterns
that may herald a change of bank or even life changes—such as a move to
another city—that could result in a different bank being chosen. It may reveal,
for example, a group of customers with above-average attrition rate who do
most of their banking by phone after hours when telephone response is slow.
Data mining may determine groups for whom new services are appropriate,
such as a cluster of profitable, reliable customers who rarely get cash advances
from their credit card except in November and December, when they are pre-26 CHAPTER 1| WHAT’S IT ALL ABOUT?