A new method has been developed to figure out the best number of groups in the K-means clustering algorithm. Instead of guessing the number of clusters, this method uses a specific index to determine the optimal number. By setting limits on the number of clusters and choosing initial centers based on distance, the algorithm improves the stability and quality of clustering results. Simulation experiments show that this approach works well in practice.