基于改进K均值聚类算法的星点聚类研究

针对高分辨率天文图像中的星点聚类研究中存在的 2 个问题：①天文图像的分辨率 较高，且图像处理速度较慢；②选取何种聚类算法对天文图像中的星点进行聚类分析效果较好。 在研究中，问题 1 采用图像分块的方法提高图像的处理速度；问题 2 提出了一种改进的 K 均值聚 类算法，以解决传统的 K 均值聚类算法的聚类结果易受到 k 值和初始聚类中心随机选择影响的问 题。该算法首先在用 K 均值聚类算法对数据初步聚类的基础上确定合适的 k 值，其次用层次聚类 对数据聚类确定初始聚类中心，最后在此基础上再采用 K 均值聚类算法进行聚类。通过 MATLAB 仿真实验的结果表明，该算法的聚类结果与效率优于其他聚类算法。

Star Point Clustering Based on Improved K-Means Clustering Algorithm

Two problems in the study of star point clustering in high resolution astronomical images: ① The resolution of the astronomical image is higher, and the image processing speed is slower. ② Which clustering algorithm is selected to cluster the star points in the astronomical image is better. In the research, problem 1 uses image segmentation method to improve image processing speed. problem 2 proposes an improved K-means clustering algorithm to solve the traditional K-means clustering algorithm clustering results are susceptible to k-value and The initial clustering center randomly selects the problem of impact. Firstly, the K-means clustering algorithm is used to determine the appropriate k-value based on the preliminary clustering of data. Secondly, the clustering is used to determine the initial clustering center by data clustering. Finally, K-means clustering is used. The algorithm performs clustering. The simulation results of MATLAB show that the clustering results and efficiency of the algorithm are better than other clustering algorithms.