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.
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