K-means Method Based on the Approximate Backbone and the Shuffled Frog Leaping Algorithm and Its Application in Fundus Medical Record Images - SUN Ying;ZHANG Yi;DING Weiping;JU Hengrong;REN Longjie (Nantong Daxue xuebao. Ziran kexue ban)
In order to improve the global search power of the K-means algorithm and the clustering effect, the K-means method based on the approximate backbone and the shuffled frog leaping algorithm is proposed. Firstly, in this method, the classic iterative formula of the K-means algorithm is replaced by the classic shuffled frog leaping algorithm to obtain multiple sets of better clustering results. Then, the K-means algorithm based on the approximate backbone and the shuffled frog leaping algorithm is used for the obtained clustering results. Instead of searching for cluster centers, the cluster division is directly modified. The experimental results of the UCI dataset reveal that the clustering results obtained by the improved algorithm are better than those of other algorithms. Finally, this study applies the improved clustering algorithm to the medical fundus medical records images, which has a better effect on the vascular cutting.