Model transformations play a fundamental role in model-driven software development. They can be used to solve or support central tasks, such as creating models, handling model co-evolution, and model merging. In the past, various (semi-)automatic approaches have been proposed to derive model transformations from meta-models or from examples. These approaches require time-consuming handcrafting or recording of concrete examples, or they are not able to derive complex transformations. We propose a novel unsupervised approach, called OCKHAM, which is able to learn edit operations from models in model repositories. OCKHAM is based on the idea that a meaningful edit operation will be one that can compress the model differences. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We find that our approach is able to discover frequent edit operation that have actually been applied. Furthermore, OCKHAM is able to extract edit operations in an industrial setting that are meaningful to practitioners.