针对传统的K均值算法对初始聚类中心的选取和孤立点敏感的问题，本文提出了一种基于点密度的初始聚类中心选取方法。利用该方法选出初始聚类中心，再应用K均值算法进行聚类，同时对孤立点进行特殊处理。实验表明，该方法能够产生高质量的聚类结果。

Traditional Kmeans clustering algorithms are sensitive to the selection of initial clustering centers and isolated points. Considering these problems, a new method based on the density of points is presented in this paper. First of all, we select initial clustering centers through the proposed method. Then, we apply a Kmeans clustering algorithm to cluster the data, and process the isolated points especially. The experimental results demonstrate that the proposed method can get better clustering results.