论文标题
连续变量的等级层次聚类的差异函数
Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables
论文作者
论文摘要
研究了一个理论框架，以研究连续随机向量及其主要特性之间的（基于copula的）差异。所提出的差异将最小的值分配给了一对共连的随机向量。研究了这种差异的各种特性，并特别注意那些容易出现分层聚集方法的特性，例如可降低性。提供了一些洞察力，用于在聚类算法中使用这种度量，并提供了模拟研究。实际案例研究说明了整个方法的主要特征。
A theoretical framework is presented for a (copula-based) notion of dissimilarity between continuous random vectors and its main properties are studied. The proposed dissimilarity assigns the smallest value to a pair of random vectors that are comonotonic. Various properties of this dissimilarity are studied, with special attention to those that are prone to the hierarchical agglomerative methods, such as reducibility. Some insights are provided for the use of such a measure in clustering algorithms and a simulation study is presented. Real case studies illustrate the main features of the whole methodology.
