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FocalPoint: Two-Stage k-Means Method
for Optimum ESS Cluster Models
David Wishart
Department of Management, University of St. Andrews
St Katharine's West, The Scores, St. Andrews KY16 9AL, Scotland
The paper describes FocalPoint, a two-stage k-means method to search for optimum ESS cluster models.
The first stage completes k-means analysis with six different starting strategies and randomized trials, to test the sensitivity of the starting solution and case order. Differential variable and case weights can be
specified, outliers and intermediate cases can be removed, and clustering can be around cluster means or exemplars. An exact relocation test on the Euclidean Sum of Squares (ESS) assures convergence.
The top solutions found at the first stage are saved, ordered by goodness-of-fit, and their reproducibility is estimated. The second stage involves cluster model profiling, evaluation, selection, and calibration.
Variable weights can be revised by t-tests on cluster means or F-tests on variances, so as to emphasise the most discriminating variables. When a final cluster model has been chosen, it can be summarizing
by hierarchical cluster analysis, with optimal cluster ordering, and outlier assignment. FocalPoint will be illustrated by a market segmentation of 