九种流行聚类方法的实证比较和特征
An empirical comparison and characterisation of nine popular clustering methods
摘要：
九个流行的聚类方法被应用到42个真实数据集。其目的是通过几个聚类验证指数来详细描述方法的特征，这些指标可以测量所得聚类的各个方面，例如小的聚类内距离，聚类分离，接近高斯分布等，如Hennig（2019）所介绍的。其中30个数据集具有“真实”聚类。在这些数据集的相似性的聚类从九种方法的“真正的”聚类进行了探讨。此外，混合效应回归将聚类的可观察的个体方面与“真实”聚类的相似性联系起来，这在真实的聚类问题中是不可观察的。这项研究提供了新的见解，不仅发现“真正的”聚类的方法的能力，但也到聚类的属性，可以预期的方法，这是至关重要的选择一种方法，在一个真正的情况下，没有一个给定的“真正的”聚类。
英文摘要：
Nine popular clustering methods are applied to 42 real data sets. The aim is to give a detailed characterisation of the methods by means of several cluster validation indexes that measure various individual aspects of the resulting clusters such as small within-cluster distances, separation of clusters, closeness to a Gaussian distribution etc. as introduced in Hennig (2019). 30 of the data sets come with a "true" clustering. On these data sets the similarity of the clusterings from the nine methods to the "true" clusterings is explored. Furthermore, a mixed effects regression relates the observable individual aspects of the clusters to the similarity with the "true" clusterings, which in real clustering problems is unobservable. The study gives new insight not only into the ability of the methods to discover "true" clusterings, but also into properties of clusterings that can be expected from the methods, which is crucial for the choice of a method in a real situation without a given "true" clustering.
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。
