Mathematical Sciences: General Global Criteria for SelectingClustering Algorithms

该项目将制定全球标准， 聚类程序使用的容许聚类方法 Fisher和货车Ness与可容许性的扩展定义 条件 这些受理条件将适用于 广义无限参数化聚类族 Lance和威廉姆斯提出的程序;以及 条件，族参数的相应子集 将确定产生可接受算法的空间。 集群是一种用于划分组的常用工具 将物体划分为亚类或种类。 它被分类学家使用， 图书馆员、流行病学家、银行家、科学家和其他许多人 把大量复杂的物体分类成同质的 可以更好地理解和处理的群体。 许多 聚类方法已经提出了多年，每一个 给出不同的聚类;因此，算法的选择是 重要. 这里的工作是将各种算法编成法典 根据具体应用所需的性质来选择。

This project will develop global criteria for selecting clustering procedures using the admissible clustering approach of Fisher and Van Ness with extended definitions of admissibility conditions. These admissibility conditions will be applied to the general infinite parameterized family of clustering procedures proposed by Lance and Williams; and for each condition, the corresponding subset of the family's parameter space yielding admissible algorithms will be determined. Clustering is a heavily used tool for partitioning a group of objects into subgroups or species. It is used by taxonomists, librarians, epidemiologists, bankers, scientists and many others to classify large, complex sets of objects into homogeneous groups which can be comprehended and handled better. Numerous clustering methods have been proposed over the years, with each giving a different clustering; hence, the choice of algorithm is important. The work here is to codify the various algorithms according to the properties needed for a specific application.