Book Image

IBM SPSS Modeler Cookbook

Book Image

IBM SPSS Modeler Cookbook

Overview of this book

IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork. IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art. Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace. Go beyond the basics and get the full power of your data mining workbench with this practical guide.
Table of Contents (11 chapters)
10
Index

Shuffle-down (nonstandard aggregation)


Some applications require a form of aggregation not directly supported in Modeler, for example, aggregating a set of Booleans or Flags with a logical or Boolean operator such as OR. This recipe shows how to combine sorting with sequence functions in filler nodes to perform nonstandard aggregations.

This technique originates with basket analysis using versions of Modeler that pre-date the inclusion of the SetToflag node. Without the Set-to-flag node, when an analysis required aggregating a set of Booleans, it was necessary to construct the required technique out of the then-existing nodes. Typically, this processing step started with multiple records for each customer, with several fields representing the presence of different products in the basket, one basket per record. It is then required to perform an aggregation using a Boolean OR operation to produce one record per customer showing the products in all the baskets (that is, whether the customer...