An improved seeds scheme in K‐means clustering algorithm for the UAVs control system application

Clustering algorithm is the primary technology used in target clustering and group status analysis which are key features of the Unmanned Aerial Vehicles (UAVs) control system. Due to variable application environment, the stability of the algorithm in the UAVs control system needs to be considered. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. For this problem, the predecessors have proposed various effective solutions. These algorithms perform better on real and large‐scale datasets, but they are unable to achieve optimum results with unbalanced datasets. Herein, a simpler and more effective algorithm for seed initialization is proposed, it has a better accuracy rate than the alternative algorithms.Moreover, after running tests multiple times with each algorithm independently, it has the highest stability and the lowest overall volatility. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets. K‐means clustering is a widely used method in intelligent systems. However, K‐means algorithm is susceptible to the local optimum due to the influence of the initial centroid. Herein, a simpler and more effective algorithm for seed initialization is proposed. With unbalanced datasets, the proposed algorithm performs significantly better than several other algorithms and therefore can solve the problems that other algorithms have with unbalanced datasets.