Abstract
In many situations, one wishes to group objects into well-defined classes on the basis of one set of descriptor variables, and then predict the classes of new objects from a different set of variables. For example, a bank may categorise customers into distinct financial behaviour pattern classes by observing how they have behaved over a period of years, and then seek to assign new customers to future behaviour classes using information captured when they open an account. Such situations require the striking of a compromise between the compactness and integrity of the cluster structure, and the accuracy of the predictive assignment to clusters. We describe two algorithms for achieving such a compromise, discuss some of their features, and illustrate their performance in a simulation study and in a liver transplant problem.
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Hand, D.J., Krzanowski, W.J. & Crowder, M.J. Optimal predictive partitioning. Stat Comput 17, 11–21 (2007). https://doi.org/10.1007/s11222-006-9003-x
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DOI: https://doi.org/10.1007/s11222-006-9003-x