This study performed data mining for nondominated-solution datasets of flyback-booster geometry for next-generation space transportation procured by evolutionary computation. We prepared two datasets of nondominated solutions due to two problem definitions, which differ merely in the definition of some design variables based on a design hypothesis gained from evolutionary-computation results. This study aims at verifying the hypothesis by applying mining to these two datasets to elucidate the contrast in the influence of the design variables. We used functional analysis of variance for data mining; scrutinized the effects of single and two-combined design variables. Furthermore, intuitive visualization by triangular matrix representations could distinguish the discrepancy between the obtained results. The consequence has verified the significance of the hypothesis; it revealed that the discontinuous surface naturally evaded in the hypersonic range because of surface temperature upsurge is capable of enhancing the lift-to-drag ratio in the low-speed range; the hypothesis grew into a new design problem.