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Classification procedures are common and useful in behavioral, educational, and social research. Supervised classification techniques like discriminant function analysis (DFA) assume training data is perfectly classified when estimating parameters and classification. In contrast, unsupervised classification techniques like finite mixture models (FMM) do not require

The Effects of Initially Misclassified Data on the Effectiveness of Discriminant Function Analysis and Finite Mixture Modeling