An Improved SFLA-Kmeans Algorithm Based on Approximate Backbone and Its Application in Retinal Fundus Image

Abstract

In order to improve the global search ability of K-means algorithm and the clustering effect, a K-means method based on the approximate backbone and the shuffled frog leaping algorithm was proposed. Firstly, the classic iterative formula of the K-means algorithm is replaced by the classic shuffled frog leaping algorithm to obtain better clustering results. Secondly, 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. Finally, the experimental results on the UCI dataset show that, the running time of the improved clustering algorithm is shorter than that based on the shuffled frog leaping algorithm only, and clustering results obtained by using the improved clustering algorithm are better than those of other algorithms. In addition, the paper uses the improved clustering algorithm to preprocess medical fundus images to optimize the effect of vascular cutting.

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