A common problem with most of the feature selection methods is that they often produce feature sets-models-that are not stable with respect to slight variations in the training data. Different authors tried to improve the feature selection stability using ensemble methods which aggregate different feature sets into a single model. However, the existing ensemble feature selection methods suffer from two main shortcomings: (i) the aggregation treats the features independently and does not account for their interactions, and (ii) a single feature set is returned, nevertheless, in various applications there might be more than one feature sets, potentially redundant, with similar information content. In this work we address these two limitations. We present a general framework in which we mine over different feature models produced from a given dataset in order to extract patterns over the models. We use these patterns to derive more complex feature model aggregation strategies that account for feature interactions, and identify core and distinct feature models. We conduct an extensive experimental evaluation of the proposed framework where we demonstrate its effectiveness over a number of high-dimensional problems from the fields of biology and text-mining.