Abstract
One of the thorniest aspects of cluster analysis continues to be the weighting and selection of variables. This paper reports on the performance of nine methods on eight “leading case” simulated and real sets of data. The results demonstrate shortcomings of weighting based on the standard deviation or range as well as other more complex schemes in the literature. Weighting schemes based upon carefully chosen estimates of within-cluster and between-cluster variability are generally more effective. These estimates do not require knowledge of the cluster structure. Additional research is essential: worry-free approaches do not yet exist.
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Gnanadesikan, R., Kettenring, J.R. & Tsao, S.L. Weighting and selection of variables for cluster analysis. Journal of Classification 12, 113–136 (1995). https://doi.org/10.1007/BF01202271
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DOI: https://doi.org/10.1007/BF01202271