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Asymptotical optimality in cluster analysis

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  • Yurij S. Kharin
  • Eugene E. Zhuk

Abstract

The problem of optimality and performance evaluation for cluster analysis procedures is investigated. For the situations where the classes are described by known or unknown prior probabilities and regular probability density functions with unknown parameters the asymptotic expansions of classification error probability are constructed. The results are illustrated for the case of well‐known Fisher classification model. Copyright © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • Yurij S. Kharin & Eugene E. Zhuk, 1999. "Asymptotical optimality in cluster analysis," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 15(1), pages 65-75, March.
    • Handle: RePEc:wly:apsmda:v:15:y:1999:i:1:p:65-75
    DOI: 10.1002/(SICI)1099-0747(199903)15:13.0.CO;2-Q
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