Chapter 1. Dimension Reduction Methods

Summary

Chapter one begins with an assessment of the need for dimension reduction in data mining. Principal components analysis is demonstrated, in the context of a real-world example using the Houses data set. Various criteria are compared for determining how many components should be extracted. Emphasis is given to profiling the principal components for the end-user, along with the importance of validating the principal components using the usual hold-out methods in data mining. Next, factor analysis is introduced and demonstrated using the real-world Adult data set. The need for factor rotation is discussed, which clarifies the definition of the factors. Finally, user-defined composites are briefly discussed, using an example.