Experimental and calculated values of the activation energies of elementary steps provide insight into the mechanisms of many important catalytic reactions. The collections of these values also present an opportunity to apply knowledge discovery techniques in order to find more general qualitative patterns that are implicit in the data. Here, we apply classical hierarchical clustering and recently developed classification profiling methods to 168 steps and activation energies relevant to carbon dioxide re-forming of methane, compiled for eight different transition metal catalysts. The core fragments of the 168 transition states are defined and used as features. These methods and features address the basic questions of what the similarities and differences among catalysts are and why they are similar or different. The consistency of the results with experimental observations suggests that the methods are reliably predictive.