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Data mining techniques on astronomical spectra data – II. Classification analysis by Haifeng Yang; Lichan Zhou; Jianghui Cai; Chenhui Shi; Yuqing Yang; Xujun Zhao; Juncheng Duan; Xiaona Yin is a Physics and Astronomy article available to read on EtoBox.

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## ABSTRACT Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of spectral surveys, a variety of classification techniques have been successfully applied to astronomical data processing. However, it is difficult to select an appropriate classification method in practical scenarios due to the different algorithmic ideas and data characteristics. Here, we present the second work in the data mining series – a review of spectral classification techniques. This work also consists of three parts: a systematic overview of current literature, experimental analyses of commonly used classification algorithms, and source codes used in this paper. First, we carefully investigate the current classification methods in astronomical literature and organize these methods into ten types based on their algorithmic ideas. For each type of algorithm, the analysis is organized from the following three perspectives. (1) their current applications and usage frequencies in spectral classification are summarized; (2) their basic ideas are introduced and preliminarily analysed; (3) the advantages and caveats of each type of algor

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Author

Haifeng Yang; Lichan Zhou; Jianghui Cai; Chenhui Shi; Yuqing Yang; Xujun Zhao; Juncheng Duan; Xiaona Yin

Publisher

Oxford University Press (OUP)

Published

2022

Language

EN

Field

Physics and Astronomy (Physical Sciences)