Organization Preference Knowledge Acquisition of Multi-Platform Aircraft Mission System Utilizing Frequent Closed Itemset Mining

Organization preference knowledge is critical to enhancing the intelligence and efficiency of multi-platform aircraft mission system (MPAMS), particularly collaboration tactics task behaviors, platform types, mount resources. However, it challenging extract such concisely, which buried in massive historical data. Therefore, this paper proposes an innovative data-driven approach via frequent closed itemset mining (FCIM) algorithm discover valuable MPAMS organizational knowledge. The proposed addresses limitations poor effectiveness low for previously discovered To ensure effectiveness, designs a multi-layer discovery framework from system-of-systems perspective, allowing more systematic than traditional frameworks considering isolated layer. Additionally, MPAMS’s contextual capability reflecting decision motivation integrated into representation, making intelligible decision-makers. Further, efficiency, process accelerated by designing storage structure three pruning strategies FCIM. simulation 1100 air-to-sea assault scenarios has provided abundant with high interpretability. performance superiority thoroughly verified comparative experiments. provides guidance insights future development organization optimization.