Statistics and Computing

, Volume 17, Issue 1, pp 11–21 | Cite as

Optimal predictive partitioning

  • David J. Hand
  • Wojtek J. KrzanowskiEmail author
  • Martin J. Crowder


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.


Alternating least squares Clustering Criterion optimisation Discrimination Error rates Transfer algorithm 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • David J. Hand
    • 1
    • 3
  • Wojtek J. Krzanowski
    • 2
    Email author
  • Martin J. Crowder
    • 1
  1. 1.Department of MathematicsImperial College of Science, Technology and MedicineLondonUK
  2. 2.School of Engineering, Computer Science and MathematicsUniversity of ExeterExeterUK
  3. 3.Institute for Mathematical SciencesImperial College of Science, Technology and MedicineLondonUK

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