For an efficient collaboration of multi-robot system during missions, it is essential for the system to create a global map and localize the robots in it. However, the relative poses among robots may be unknown, preventing the system from generating the reference map. In such cases, the necessary information must be inferred through inter-robot loop closures, which are mainly perception-derived measurements obtained when robots observe the same place. However, as perception-derived measurements rely on the similarity of sensor data, different places could be wrongly identified as the same location if they exhibit similar appearances. This phenomenon, called perceptual aliasing, produces inaccurate loop closures that can severely distort the global map. This study presents a robust inter-robot loop closure selection for map fusion that utilizes the degrees of both consistency and data similarity of the loop closures for accurate measurement determination. We define the coalition of these information as the measurement pair score and employ it as weights in the objective function of the combinatorial optimization problem that can be solved as maximum edge weight clique from graph theory. The algorithm is tested on an experimental dataset for performance evaluation and the result is discussed in comparison to a state-of-the-art method.