基于K-Means聚类算法的改进

本文基于传统的K-means聚类方法提出来一种基于密度的改进K-means聚类方法。改进后的方法，首先选取数据集中密度最大的点作为第一个聚类中心点，以此为基准，选取离此点最远的点作为第二个初始聚类中心，再在剩余的点中找出离这两个初始点距离最远的点作为第三个聚类中心，以此类推，直到找到所需的K个点，之后再根据K-means算法迭代更新聚类中心，直到收敛或达到设定的迭代次数为止。实验结果表明，本文提出的方法与传统K-means方法相比准确率及稳定性方面均有所提高，可以作为聚类研究的一个新的思路。

Improvement of clustering algorithm based on k-means

This paper presents a method of clustering which improves the stability and efficiency of K-means clustering and based on density.The first step of this method is to select the largest density point of the data set .On this basis, the farthest point from this point is selected as the second initial cluster center,Then, we look for the farthest distance from the two initial points in the remaining points as the center of the third clusters,And so on, until you find the desired K points, and then according to the K-means algorithm to update the cluster center, until convergence or Up to the set number of iterations. The result of the experiments show that the proposed method is better than the traditional K-means method in terms of accuracy and stability, and can be used as a new idea of clustering research.