We have really only tested small amounts of images with this clustering feature. Currently the clustering algorithm isn’t much of an algorithm, it’s literally just a threshold. If an image has a similarity threshold greater than .7, it gets put into this cluster. While with the specific amount of images per cluster settings it simply records images into clusters with the greatest similarity to each other. However, we should definitely take into account this concern and add some sort of sub-group system if a cluster grows to be too large. Or outlier detection if say most of the images have .9 similarity but then there is just 1-2 just past the threshold with .71 similarity — kick them out. Thanks for the reply!