基于指数母函数的集聚聚类与集体性测度
Agglomerative clustering and collectiveness measure via exponent generating function
摘要：
凝聚聚类的关键是定义两个集合之间的亲和度。提出了一种新的凝聚聚类方法，利用路径积分来定义亲和度。首先，利用路径积分和指数母函数计算边、节点和集合的路径积分描述子。然后，通过集合的路径积分描述子得到两个集合之间的亲和度。本文提出了路径积分描述子的几个好的性质。此外，我们给出了所提出的路径积分描述子的物理解释。将集合的路径积分描述符看作集合的集体性度量，集合可以是人群、羊群等运动系统，采用自驱动粒子（SDP）模型对该方法的集体性进行了测试。
英文摘要：
The key in agglomerative clustering is to define the affinity measure between two sets. A novel agglomerative clustering method is proposed by utilizing the path integral to define the affinity measure. Firstly, the path integral descriptor of an edge, a node and a set is computed by path integral and exponent generating function. Then, the affinity measure between two sets is obtained by path integral descriptor of sets. Several good properties of the path integral descriptor is proposed in this paper. In addition, we give the physical interpretation of the proposed path integral descriptor of a set. The proposed path integral descriptor of a set can be regard as the collectiveness measure of a set, which can be a moving system such as human crowd, sheep herd and so on. Self-driven particle (SDP) model is used to test the ability of the proposed method in measuring collectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
