  Evidential clustering is an approach to clustering in which
cluster-membership uncertainty is represented by a collection of
Dempster-Shafer mass functions forming an evidential partition. In this paper,
we propose to construct these mass functions by bootstrapping finite mixture
models. In the first step, we compute bootstrap percentile confidence intervals
for all pairwise probabilities (the probabilities for any two objects to belong
to the same class). We then construct an evidential partition such that the
pairwise belief and plausibility degrees approximate the bounds of the
confidence intervals. This evidential partition is calibrated, in the sense
that the pairwise belief-plausibility intervals contain the true probabilities
"most of the time", i.e., with a probability close to the defined confidence
level. This frequentist property is verified by simulation, and the practical
applicability of the method is demonstrated using several real datasets.
