We present a language agnostic, unsupervised method for inducing morphological transformations between words. The method relies on certain regularities manifest in high-dimensional vector spaces. We show that this method is capable of discovering a wide range of morphological rules, which in turn are used to build morphological analyzers. We evaluate this method across six different languages and nine datasets, and show significant improvements across all languages.