Generation of tuning constant for Mahalanobis fixed point clusters.
Generates tuning constants
fixmahal dependent on
the number of points and variables of the current fixed point cluster
This is experimental and only thought for use in
positive integer. Number of points.
positive integer. Number of variables.
integer larger than 1. Smallest number of points for which
positive number. Minimum value for
positive integer. Number of points at which
positive numeric. Tuning constant for
numeric between 0 and 1. 1 for steepest possible descent of
Some experiments suggest that the tuning constant
decrease with increasing FPC size and increase with increasing
fixmahal. This is to prevent too small
meaningless FPCs while maintaining the significant larger
ca in such a way
that as long as
ca>cmin, the decrease in
n is as steep
as possible in order to maintain the validity of the convergence
theorem in Hennig and Christlieb (2002).
A numeric vector of length
n, giving the values for
for all FPC sizes smaller or equal to
Hennig, C. and Christlieb, N. (2002) Validating visual clusters in large datasets: Fixed point clusters of spectral features, Computational Statistics and Data Analysis 40, 723-739.
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