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.

翻译：提出了一个理论框架,用于研究连续随机矢量及其主要特性之间差异的(基于光谱的)概念,拟议的差异为一对共聚体随机矢量定出最小值,研究这种差异的各种特性,特别注意容易采用等级组合法的特性,如可减少性,为在组合算法中使用这种计量提供了一些见解,并进行了模拟研究,真实的案例研究说明了整个方法的主要特点。