The correctness rate is the estimator for the correctness of a classification rule (1-error rate).
The accuracy is based on the euclidean distances between (scaled) membership vectors and the vectors
representing the true class corner. These distances are standardized so that a measure of 1 is achieved
if all vectors lie in the correct corners and 0 if they all lie in the center.
Analougously, the ability to seperate is based on the distances between (scaled) membership
vectors and the vector representing the corresponding assigned class corner.
The confidence is the mean of the membership values of the assigned classes.
References
Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces.
Dissertation, University of Dortmund.
URL http://hdl.handle.net/2003/2789