K-Means Clustering Sorting Algorithm Optimized by SSA Based on Emitter PDW

In order to solve the problem that the initial K value of K-Means clustering algorithm needs to be given in advance and the division needs to be determined according to the initial clustering center, which is not conducive to practical application, a SSA optimized K-Means clustering sorting algorithm based on emitter PDW is proposed, which belongs to the unsupervised type according to emitter PDW data sorting. The Initial clustering center is optimized quickly by using the Silhouette Coefficient of internal evaluation index combined with the better global search and local development ability of SSA sparrow search algorithm through traversing a certain range of K values to find the optimal Silhouette Coefficient of internal evaluation index. Therefore, the K-Means algorithm is optimized without parameters, and then the K-Means algorithm is used for clustering. The proposed clustering sorting algorithm is verified by simulation data, and it has high correctness for the clustering effect in the presence of interference.