基于拉普拉斯中心性和密度峰值的无参数聚类算法

针对聚类算法的聚类中心选取需要人工参与的问题，提出了一种基于拉普拉斯中心性和密度峰值的无参数聚类算法（ALPC）。首先，使用拉普拉斯中心性度量对象的中心性；然后，使用正态分布概率统计方法确定聚类中心对象；最后，依据对象到各个中心的距离将各个对象分配到相应聚类中心实现聚类。所提算法克服了算法需要凭借经验参数和人工选取聚类中心的缺点。在人工数据集和真实数据集上的实验结果表明，与经典的具有噪声的基于密度的聚类方法（DBSCAN）、密度峰值聚类（DPC）算法以及拉普拉斯中心峰聚类（LPC）算法相比，ALPC具有自动确定聚类中心、无参数的特点，且具有较高的聚类精度。

Parameter-free clustering algorithm based on Laplace centrality and density peaks

In order to solve the problem of selecting center manually in a clustering algorithm, a Parameter-free Clustering Algorithm based on Laplace centrality and density peaks (ALPC) was proposed. Laplace centrality was used to measure the centrality of objects, and a normal distribution probability statistical method was used to determine clustering centers. The problem that clustering algorithms rely on empirical parameters and manually determine cluster centers was solved by the proposed algorithm. Each object was assigned to the corresponding cluster center according to the distance between the object and the center. The experimental results on synthetic data sets and UCI data sets show that the new algorithm can not only automatically determine cluster centers without any priori parameters, but also get better results with higher accuracy compared with the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm, Clustering by fast search and find of Density Peaks (DPC) algorithm and Laplace centrality Peaks Clustering (LPC) algorithm.