Data Mining: Practical Machine Learning Tools and ... - LIDeCC

26 CHAPTER 1 | WHAT’S IT ALL ABOUT?whether or not a fault existed, but to diagnose the kind of fault, given that onewas 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 intermediateconcepts, that is, functions of basic attributes, which were defined inconsultation with the expert and embodied some causal domain knowledge.The derived attributes were run through an induction algorithm to produce aset of diagnostic rules. Initially, the expert was not satisfied with the rulesbecause he could not relate them to his own knowledge and experience. Forhim, mere statistical evidence was not, by itself, an adequate explanation.Further background knowledge had to be used before satisfactory rules weregenerated. Although the resulting rules were quite complex, the expert likedthem because he could justify them in light of his mechanical knowledge. Hewas pleased that a third of the rules coincided with ones he used himself andwas delighted to gain new insight from some of the others.Performance tests indicated that the learned rules were slightly superior tothe handcrafted ones that had previously been elicited from the expert, and thisresult was confirmed by subsequent use in the chemical factory. It is interestingto note, however, that the system was put into use not because of its good performancebut because the domain expert approved of the rules that had beenlearned.Marketing and salesSome of the most active applications of data mining have been in the area ofmarketing and sales. These are domains in which companies possess massivevolumes of precisely recorded data, data which—it has only recently been realized—ispotentially extremely valuable. In these applications, predictions themselvesare the chief interest: the structure of how decisions are made is oftencompletely irrelevant.We have already mentioned the problem of fickle customer loyalty and thechallenge of detecting customers who are likely to defect so that they can bewooed back into the fold by giving them special treatment. Banks were earlyadopters of data mining technology because of their successes in the use ofmachine learning for credit assessment. Data mining is now being used toreduce customer attrition by detecting changes in individual banking patternsthat may herald a change of bank or even life changes—such as a move toanother 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 domost 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 advancesfrom their credit card except in November and December, when they are pre-