Chapter 1. Dimension Reduction Methods

  1. Daniel T. Larose Ph.D. Director

Published Online: 30 JAN 2006

DOI: 10.1002/0471756482.ch1

Data Mining Methods and Models

Data Mining Methods and Models

How to Cite

Larose, D. T. (2006) Dimension Reduction Methods, in Data Mining Methods and Models, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471756482.ch1

Author Information

  1. Department of Mathematical Sciences, Central Connecticut State University, USA

Publication History

  1. Published Online: 30 JAN 2006
  2. Published Print: 11 NOV 2005

ISBN Information

Print ISBN: 9780471666561

Online ISBN: 9780471756484

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Keywords:

  • principal components;
  • factor analysis;
  • commonality;
  • variation;
  • scree plot;
  • eigenvalues;
  • component weights;
  • factor loadings;
  • factor rotation;
  • user-defined composite

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.