  We introduce geometric pattern mining, the problem of finding recurring local
structure in discrete, geometric matrices. It differs from existing pattern
mining problems by identifying complex spatial relations between elements,
resulting in arbitrarily shaped patterns. After we formalise this new type of
pattern mining, we propose an approach to selecting a set of patterns using the
Minimum Description Length principle. We demonstrate the potential of our
approach by introducing Vouw, a heuristic algorithm for mining exact geometric
patterns. We show that Vouw delivers high-quality results with a synthetic
benchmark.
