In 2017, the Muon Hunter project on the Zooniverse.org citizen science platform successfully gathered more than two million classification labels for nearly 140,000 camera images from VERITAS. The aim was to select and parameterize muon events for use in training convolutional neural networks. The success of this project proved that crowdsourcing labels for IACT image analysis is a viable avenue for further development of advanced machine-learning algorithms. These algorithms could potentially lend themselves to improving class separation between gamma-ray and hadronic event types. Nonetheless, it took two months to gather these labels from volunteers, which could be a bottleneck for future applications of this method. Here we present Muon Hunters 2.0: the follow-on project that demonstrates the development of unsupervised clustering techniques to gather muon labels more efficiently from volunteer classifiers.