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

Optimizing K-means cluster solutions


K-means clustering is a well-established technique for grouping entities together based on overall similarity. It has many applications including customer segmentation, anomaly detection (finding records that don't fit into existing clusters), and variable reduction (converting many input variables into fewer composite variables).

For all its power and popularity, the K-means algorithm does have a number of known limitations. First, the K-means algorithm is iterative and can arrive at many possible solutions based on the data and the initial algorithm parameters. Some solutions may be better than other solutions and the final solution generally depends on the choice for the location of the initial cluster centers. In most implementations of K-means (including the Modeler implementation), the initial centers depend on the ordering of the data. Thus the quality of the clusters depends on the order of the data during modeling. Second, the K-means algorithm...