The purpose of this paper is to improve the traditional K-means algorithm. In the traditional K mean clustering algorithm, the initial clustering centers are generated randomly in the data set. It is easy to fall into the local minimum solution when the initial cluster centers are randomly generated. The initial clustering center selected by K-means clustering algorithm which based on density is more representative. The experimental results show that the improved K clustering algorithm can eliminate the dependence on the initial cluster, and the accuracy of clustering is improved.
翻译:本文的目的是改进传统的 K 手段算法。 在传统的 K 平均组合算法中, 最初的集束中心是在数据集中随机生成的。 当最初的集束中心是随机生成的, 很容易落到本地最低解决方案中。 最初的集束中心是由 K 手段组算法选择的, 以密度为基础, 具有更大的代表性 。 实验结果显示, 改进的 K 组合算法可以消除对初始集集的依赖性, 组合的准确性也得到了提高 。