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IBM SPSS Modeler Cookbook by Scott Mutchler, Tom Khabaza, Meta S. Brown, Dean Abbott, Keith McCormick

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Sequence processing

Many applications require the discovery of patterns in data representing a sequence of events; examples include quality control and fault diagnosis and prevention in industrial and mechanical processes. Data in these applications typically takes the form of logs; that is time-stamped sets of measurements that form a sequence. The measurements may be very simple, even a single variable, but the patterns are found in how these measurements vary over time. Modeler includes a variety of features for processing sequential data of this sort. This recipe illustrates some of these sequence processing operations and how they are used to build up a set of variables describing the changes in measurement over time.

Getting ready

This recipe ...

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