【摘要】 为解决用户群体移动轨迹划分和密度峰值聚类算法自身局限性的问题，以校园轨迹为对象，考虑时间和位置语义信息层面的信息，建立网络用户间的相似性度量模型，提出一种基于共享近邻贡献度的密度峰值聚类算法(density peak clustering based on shared nearest neighbor contribution, SNNC-DPC),结合信息熵理论，通过最小化局部密度熵自适应选择截断距离；在局部密度计算上，利用共享近邻贡献度重新计算局部密度，更加全面地反映数据分布的特性；采用非线性变换方法选取决策值，解决聚类中心选取困难且方法单一的问题。在真实校园轨迹数据集上实验，验证了改进算法的有效性。更多还原

【Abstract】 To solve the problems of the limitations of user group moving trajectory division and density peak clustering algorithm, taking the campus trajectory as the object, considering the information of time and location semantic information, a similarity measurement model among network users was established, and a density peak clustering based on shared nearest neighbor contribution(SNNC-DPC) algorithm was proposed, in which the information entropy theory was combined, the truncation distance was adaptively selected by minimizing the local density entropy. In the local density calculation, the shared nearest neighbor contribution was used to recalculate the local density, which reflected the characteristics of data distribution more comprehensively. The decision value was selected using the nonlinear transformation method to solve the problem of difficult and single method for selecting the clustering center. Experiments on real campus trajectory data sets verify the effectiveness of the improved algorithm.更多还原