基于欧氏距离的K-MEANS算法优化
Optimization of K-means algorithm based on Euclidean distance
对于传统的K-means聚类算法而言，在使用上有太多的局限性。文中针对K-means算法，在基于欧氏距离相似度计算的基础上，利用现有的一些算法，对聚类值k大小的判断和个初始类聚中心的选取这两方面进行了相应的优化。通过MATLAB工具进行数据测试实验，使用轮廓系数来衡量算法结果，将优化前及优化后从不同方面作比较，分析结果。证明该算法优化后的表现更佳。
For the traditional K-means clustering algorithm, there are too many limitations in its use,In this paper, based on Euclidean distance similarity calculation, the K-means algorithm is used to optimize the judgment of clustering value K and the selection of individual initial clustering centers.Data test experiments are carried out with MATLAB tools, and the results of the algorithm are measured with Silhouette Coefficient.The results are compared from different aspects before and after optimization. It is proved that the optimized algorithm performs better.
邓健、宋文广、张伟委、沈翀、李轮
计算技术、计算机技术
聚类分析K-means相似度计算K-means++轮廓系数
cluster analysisK-meanssimilarity alculationK-means++Silhouette Coefficient
邓健,宋文广,张伟委,沈翀,李轮.基于欧氏距离的K-MEANS算法优化[EB/OL].(2019-05-14)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/201905-140.点此复制
评论