Create evaluation functions for compare_clusterings().
cvi_evaluators(type = "valid", fuzzy = FALSE, ground.truth = NULL)A list with two functions: score and pick.
A character vector with options supported by cvi().
Logical indicating whether to use fuzzy CVIs or not.
A vector that can be coerced to integers used for the calculation of external
CVIs (passed as b to cvi()).
Think of this as a factory for compare_clusterings() that creates functions that can be passed
as its score.clus and pick.clus arguments. It is somewhat limited in scope because it depends
on the cluster validity indices available in cvi() for scoring and performs majority voting
for picking. They always assume that no errors occurred.
The scoring function takes the CVIs that are to be minimized and "inverts" them by taking their
reciprocal so that maximization can be considered uniformly for the purpose of majority voting.
Its ellipsis (...) is passed to cvi().
The picking function returns the best configuration if return.objects is FALSE, or a list
with the chosen TSClusters object and the corresponding configuration otherwise.
Refer to the examples in compare_clusterings().