密度峰值快速聚类算法优化研究

密度峰值快速搜索聚类CFSFDP算法选择聚类中心时需要通过人工在决策图中选择，且最后进行簇核心与簇光晕划分时会将簇的一些边缘部分划入簇光晕中，导致划分结果不够合理。针对以上问题，提出一种聚类中心自动选择及簇核心与簇光晕分割优化的聚类算法。利用异常检测的思想，寻找簇中心权值的异常点，将异常点作为各簇的聚类中心；引入簇内局部密度，实现对簇核心与簇光晕更合理的分割。通过实验对比，本文提出的算法自动化效果优于CFSFDP算法且得到的聚类结果更为精确。

Optimization of clustering by fast search and find of density peaks

The cluster center is chosen manually in the decision graph of the clustering by fast search and find of peaks (CFSFDP) algorithm. And when the cluster is divided into cluster core and cluster halo, some points on the edge of the cluster are divided into the cluster halo group, leading to unreasonable division results. To solve the above problem, we propose a clustering algorithm for automatic selection of cluster center and optimization of the division of cluster core and cluster halo. We adopt the idea of anomaly detection to find the anomaly points of the cluster center. The anomaly points are regarded as the cluster center. And local density in the cluster is introduced for the optimization of the division of cluster core and cluster halo. Comparative experiments show that the proposed algorithm is superior to the CFSFDP algorithm in automation effect and the clustering results are more accurate.