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.
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Schaffer, C.M., Green, P.E., Carmone, F.J. (2015). An Empirical Assessment of Two Univariate Screening Measures in Cluster Analysis. In: Manrai, A., Meadow, H. (eds) Global Perspectives in Marketing for the 21st Century. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17356-6_36
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