论文标题
天文学的分层聚类
Hierarchical Clustering in Astronomy
论文作者
论文摘要
分层聚类是数据分析中的常见算法。它在许多聚类算法中是独一无二的，因为它根据某个度量标准的数据距离绘制树状图，并将其分组。它被广泛用于天文学研究的所有领域，涵盖了从小行星和分子云到星系和星系群的各种尺度。本文系统地回顾了天文学各个分支中分层聚类方法发展的历史和当前状态。这些应用可以分为两个广泛的类别，一个揭示了天体系统的内在分层结构，而另一个揭示了自动对天体对象的大型样本进行分类。通过审查这些应用，我们可以阐明分层聚类算法的条件和局限性，并做出更合理和可靠的天文学发现。
Hierarchical clustering is a common algorithm in data analysis. It is unique among many clustering algorithms in that it draws dendrograms based on the distance of data under a certain metric, and group them. It is widely used in all areas of astronomical research, covering various scales from asteroids and molecular clouds, to galaxies and galaxy cluster. This paper systematically reviews the history and current status of the development of hierarchical clustering methods in various branches of astronomy. These applications can be grouped into two broad categories, one revealing the intrinsic hierarchical structure of celestial systems and the other classifying large samples of celestial objects automatically. By reviewing these applications, we can clarify the conditions and limitations of the hierarchical clustering algorithm, and make more reasonable and reliable astronomical discoveries.
