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An Empirical Assessment of Two Univariate Screening Measures in Cluster Analysis

  • Catherine M. Schaffer
  • Paul E. Green
  • Frank J. CarmoneJr.
Conference paper
Part of the Developments in Marketing Science: Proceedings of the Academy of Marketing Science book series (DMSPAMS)

Abstract

As researchers soon discover, the inclusion of noisy (irrelevant) variables in cluster analyses can obscure or distort Atrue@ subgroup structures. This problem, identified and discussed by Milligan (1980), has prompted the search for methods that identify noisy variables and either down-weight or remove them. Several researchers have investigated this problem and have met with limited success (DeSarbo, Carroll, Clark, and Green 1984, De Soete 1986, 1988). Recently, Donoghue (1995) and Carmone, Kara, and Maxwell (forthcoming, 1999) have proposed screening methods to identify and eliminate noisy variables.

Keywords

Cluster Structure Variable Weighting Heuristic Identification Multivariate Behavioral Research Subgroup Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Academy of Marketing Science 2015

Authors and Affiliations

  • Catherine M. Schaffer
    • 1
  • Paul E. Green
    • 1
  • Frank J. CarmoneJr.
    • 2
  1. 1.University of PennsylvaniaNewarkUSA
  2. 2.Wright State UniversityNewarkUSA

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