基于密度的改进K均值算法及实现

传统的K均值算法的初始聚类中心从数据集中随机产生，聚类结果很不稳定。提出一种基于密度算法优化初始聚类中心的改进K-means算法，该算法选择相互距离最远的k个处于高密度区域的点作为初始聚类中心。实验证明，改进的K-means算法能够消除对初始聚类中心的依赖，聚类结果有了较大的改进。

Improved K-means algorithm and its implementation based on density

The initial clustering center of the traditional K-means algorithm was generated randomly from the data set, and the clustering result was unstable. An improved K-means algorithm based on density algorithm optimizing initial clustering center was proposed, which selected the furthest mutual distance k points in high density region as the initial centers. The experimental results demonstrate that the improved K-means algorithm can eliminate the dependence on the initial cluster center, and the clustering result has been greatly improved.