The Marine Corps' Project Albert seeks to model complex phenomenon by observing the behavior of relatively simple simulations over thousands of runs. These simulations are based upon lightweight agents, whose essential behavior has been distilled down to a small number of rules. By varying the parameters of these rules, Project Albert simulations can explor emergent complex nonlinear behaviors with the aim of developing insight not readily provided by first principle mathematical models. Thousands of runs of Albert simulation models create large amount of data that describe the associations/correlation between the simulation input and output parameters. Understanding the associations between the simulation input and output parameters is critical to understanding the simulated complex phenomenon. This paper presents a data mining approach to analyzing the large scale and highly uncertain Albert simulation data. Specifically, a characteristic rule discovery algorithm is described in the paper together with its application to the Albert simmulation runtime data.