Cluster comparison based on CVIs
Create evaluation functions for compare_clusterings().
cvi_evaluators(type = "valid", fuzzy = FALSE, ground.truth = NULL)
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. 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().
A list with two functions: score and pick.
To avoid ambiguity, if this function is used, configurations for both fuzzy and crisp clusterings
should not be provided in the same call to compare_clusterings(). In such cases the scoring
function may fail entirely, e.g. if it was created with type = "valid".
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