A preliminary study of optimal variable weighting in k-means clustering

Recently, algorithms for optimally weighting variables in non-hierarchical and hierarchical clustering methods have been proposed. Preliminary Monte Carlo research has shown that at least one of these algorithms cross-validates extremely well.The present study applies a k-means, optimal weighting procedure to two empirical data sets and contrasts its cross-validation performance with that of unit (i.e., equal) weighting of the variables. We find that the optimal weighting procedure cross-validates better in one of the two data sets. In the second data set its comparative performance strongly depends on the approach used to find seed values for the initial k-means partitioning.

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