  Across many areas, from neural tracking to database entity resolution, manual
assessment of clusters by human experts presents a bottleneck in rapid
development of scalable and specialized clustering methods. To solve this
problem we develop C-FAR, a novel method for Fast, Automated and Reproducible
assessment of multiple hierarchical clustering algorithms simultaneously. Our
algorithm takes any number of hierarchical clustering trees as input, then
strategically queries pairs for human feedback, and outputs an optimal
clustering among those nominated by these trees. While it is applicable to
large dataset in any domain that utilizes pairwise comparisons for assessment,
our flagship application is the cluster aggregation step in spike-sorting, the
task of assigning waveforms (spikes) in recordings to neurons. On simulated
data of 96 neurons under adverse conditions, including drifting and 25\%
blackout, our algorithm produces near-perfect tracking relative to the ground
truth. Our runtime scales linearly in the number of input trees, making it a
competitive computational tool. These results indicate that C-FAR is highly
suitable as a model selection and assessment tool in clustering tasks.
