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Across many areas, from neural tracking to database entity resolution, manualassessment of clusters by human experts presents a bottleneck in rapiddevelopment of scalable and specialized clustering methods. To solve thisproblem we develop C-FAR, a novel method for Fast, Automated and Reproducibleassessment of multiple hierarchical clustering algorithms simultaneously. Ouralgorithm takes any number of hierarchical clustering trees as input, thenstrategically queries pairs for human feedback, and outputs an optimalclustering among those nominated by these trees. While it is applicable tolarge dataset in any domain that utilizes pairwise comparisons for assessment,our flagship application is the cluster aggregation step in spike-sorting, thetask of assigning waveforms (spikes) in recordings to neurons. On simulateddata of 96 neurons under adverse conditions, including drifting and 25\%blackout, our algorithm produces near-perfect tracking relative to the groundtruth. Our runtime scales linearly in the number of input trees, making it acompetitive computational tool. These results indicate that C-FAR is highlysuitable as a model selection and assessment tool in clustering tasks.

Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking