Hierarchical clustering in astronomy
Abstract
Hierarchical clustering is a common algorithm in data analysis. It is unique among several clustering algorithms as it draws dendrograms through a specific metric and extracts groups of features. It is widely used in all areas of astronomical research, covering systems at various scales, from asteroids and molecular clouds to galaxies and galaxy clusters. This paper systematically reviews the history and current status of the development of hierarchical clustering methods in various fields of astronomy. These applications can be grouped into two broad categories: one revealing the intrinsic hierarchical structure of celestial systems and the other automatically classifying large samples of celestial objects. Through cross-sectional comparisons, we can fully understand the conditions and limitations of the hierarchical clustering algorithm and generate more reasonable results in the field of astronomy.
