The article introduces a new method for clustering large-scale data more effectively. By modifying the Affinity Propagation algorithm, the researchers were able to reduce clustering time and improve accuracy. They divided the data into subsets, used a K-Affinity Propagation algorithm to select local cluster exemplars, and then applied an inverse weighted clustering algorithm to find global exemplars. This approach resulted in better clustering results compared to other algorithms like AP, KAP, and HAP.