  We apply techniques from data mining to the heterotic orbifold landscape in
order to identify new MSSM-like string models. To do so, so-called contrast
patterns are uncovered that help to distinguish between areas in the landscape
that contain MSSM-like models and the rest of the landscape. First, we develop
these patterns in the well-known $\mathbb{Z}_6$-II orbifold geometry and then
we generalize them to all other $\mathbb{Z}_N$ orbifold geometries. Our
contrast patterns have a clear physical interpretation and are easy to check
for a given string model. Hence, they can be used to scale down the potentially
interesting area in the landscape, which significantly enhances the search for
MSSM-like models. Thus, by deploying the knowledge gain from contrast mining
into a new search algorithm we create many novel MSSM-like models, especially
in corners of the landscape that were hardly accessible by the conventional
search algorithm, for example, MSSM-like $\mathbb{Z}_6$-II models with
$\Delta(54)$ flavor symmetry.
