基于密度峰值的网格聚类算法

2014年提出的密度峰值聚类算法，思想简洁新颖，所需参数少，不需要进行迭代求解，而且具有可扩展性。基于密度峰值聚类算法提出了一种网格聚类算法，能够高效地对大规模数据进行处理。首先，将N维空间粒化为不相交的长方形网格单元；然后，统计单元空间的信息，利用密度峰值聚类寻找中心点的思想确定中心单元，即中心网格单元被一些低局部密度的数据单元包围，而且与比自身局部密度高的网格单元的距离相对较大；最后，合并与中心网格单元相近网格单元，从而得出聚类结果。在UCI人工数据集上的仿真实验结果表明，所提算法能够较快得出聚类中心，有效处理大规模数据的聚类问题，具有较高的效率，与原始的密度峰值聚类算法相比，在不同数据集上时间损耗降低至原来的1/100~1/10，而精度损失维持在5%~8%。

Grid clustering algorithm based on density peaks

The Density Peak Clustering (DPC) algorithm which required few parameters and no iteration was proposed in 2014, it was simple and novel. In this paper, a grid clustering algorithm which could efficiently deal with large-scale data was proposed based on DPC. Firstly, the N dimensional space was divided into disjoint rectangular units, and the unit space information was counted. Then the central cells of space was found based on DPC, namely, the central cells were surrounded by other grid cells of low local density, and the distance with grid cells of high local density was relatively large. Finally, the grid cells adjacent to their central cells were merged to obtain the clustering results. The experimental results on UCI artificial data set show that the proposed algorithm can quickly find the clustering centers, and effectively deal with the clustering problem of large-scale data, which has a higher efficiency compared with the original density peak clustering algorithm on different data sets, reducing the loss of time 10 to 100 times, and maintaining the loss of accuracy at 5% to 8%.