The data mining technique is an important facet of solving multi-objective optimization problem because it is one of the effective manners to discover the design knowledge in the multi-objective optimization problem which obtains large data. In the present study, two data mining techniques have been performed for a large-scale, real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the influence of design variables using a self-organizing map (SOM) and a functional analysis of variance (ANOVA) to extract key features of the design space. SOM and ANOVA compensated with the respective disadvantages, and then the design knowledge could be obtained more clearly by the combination between them. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.