The goal of learning from sample data is to extract concept that captures the underlying pattern while still representing it in a way useful to the investigator. A new approach based on function decomposition in the Pattern Theory framework is presented here. The objective of this extended abstract is three-fold. The first is to provide an overview of our new approach to learning. Specifically, we wish to show the applicability to discovery. Second, we will demonstrate the correlation of decomposed function cardinality (DFC) and "patterned." Finally, we demonstrate the robustness of this approach by exhibiting experimental results on binary functions with C4.5. This new approach to discovery and learning is a powerful method for finding patterns in a robust manner.