  The validation of biclustering algorithms remains a challenging task, even
though a number of measures have been proposed for evaluating the quality of
these algorithms. Although no criterion is universally accepted as the overall
best, a number of meta-evaluation conditions to be satisfied by biclustering
algorithms have been enunciated. In this work, we present MOCICE-BCubed F$_1$,
a new external measure for evaluating biclusterings, in the scenario where gold
standard annotations are available for both the object clusters and the
associated feature subspaces. Our proposal relies on the so-called
micro-objects transformation and satisfies the most comprehensive set of
meta-evaluation conditions so far enunciated for biclusterings. Additionally,
the proposed measure adequately handles the occurrence of overlapping in both
the object and feature spaces. Moreover, when used for evaluating traditional
clusterings, which are viewed as a particular case of biclustering, the
proposed measure also satisfies the most comprehensive set of meta-evaluation
conditions so far enunciated for this task.
