  Author name disambiguation results are often evaluated by measures such as
Cluster-F, K-metric, Pairwise-F, Splitting & Lumping Error, and B-cubed.
Although these measures have distinctive evaluation schemes, this paper shows
that they can be calculated in a single framework by a set of common steps that
compare truth and predicted clusters through two hash tables recording
information about name instances with their predicted cluster indices and
frequencies of those indices per truth cluster. This integrative calculation
reduces greatly calculation runtime, which is scalable to a clustering task
involving millions of name instances within a few seconds. During the
integration process, B-cubed and K-metric are shown to produce the same
precision and recall scores. In this framework, especially, name instance pairs
for Pairwise-F are counted using a heuristic, surpassing a state-of-the-art
algorithm in speedy calculation. Details of the integrative calculation are
described with examples and pseudo-code to assist scholars to implement each
measure easily and validate the correctness of implementation. The integrative
calculation will help scholars compare similarities and differences of multiple
measures before they select ones that characterize best the clustering
performances of their disambiguation methods.
