17. Dealing with Complexity: Data Reduction and Clustering

  1. Paolo Brandimarte

Published Online: 24 MAY 2011

DOI: 10.1002/9781118023525.ch17

Quantitative Methods: An Introduction for Business Management

Quantitative Methods: An Introduction for Business Management

How to Cite

Brandimarte, P. (2011) Dealing with Complexity: Data Reduction and Clustering, in Quantitative Methods: An Introduction for Business Management, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118023525.ch17

Publication History

  1. Published Online: 24 MAY 2011
  2. Published Print: 4 APR 2011

ISBN Information

Print ISBN: 9780470496343

Online ISBN: 9781118023525

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Keywords:

  • cluster analysis;
  • data reduction;
  • factor analysis;
  • multivariate statistics;
  • principal component analysis (PCA);
  • quantitative methods

Summary

This chapter first motivates the need for data reduction; this is often a preliminary step to make the application of other quantitative methods possible. Principal component analysis (PCA) is a nice illustration of the role played by linear algebra in multivariate statistics. The chapter illustrates factor analysis, which shares some of the technical machinery of PCA. Factor analysis is an example of the statistical techniques trying to find latent variables that may help in understanding an otherwise too complicated phenomenon. The chapter outlines a range of techniques collectively known as cluster analysis. This set of methods aims at grouping observations into similar clusters, implicitly discovering some common features. Again, this has plenty of applications as tariff design and market segmentation, just to name a couple of them.

Controlled Vocabulary Terms

cluster analysis; data reduction; factor analysis; multivariate statistics; principal components analysis; quantitative forecasting