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

Abstract

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