Research of Clustering for LAMOST Early M Type Spectra

doi:10.3964/j.issn.1000-0593(2017)12-3904-04

CORC > 国家天文台 > 中国科学院国家天文台

	Research of Clustering for LAMOST Early M Type Spectra
	Liu Jie 1; Pang Jing-chang 1; Wu Ming-lei 1,3; Liu Cong 1; Wei Peng 2; Yi Zhen-ping 1; Liu Meng 1
刊名	SPECTROSCOPY AND SPECTRAL ANALYSIS
	2017-12-01
卷号	37 期号:12 页码:3904-3907
关键词	LAMOST Clustering Dimension reduction
ISSN号	1000-0593
DOI	10.3964/j.issn.1000-0593(2017)12-3904-04
英文摘要	Large-scale spectral survey projects such as LAMOST produce a great deal of valuable research data, and how to effectively analyze the data of this magnitude is a current research hotspot. Clustering algorithm is a kind of unsupervised machine learning algorithm, which makes the clustering algorithm deal with the data without knowledge of the domain, and internal law and structure will be found out. Stellar spectral clustering is a very important work in astronomical data processing. It mainly classifies the mass spectral survey data according to its physical and chemical properties. In this paper, we use a variety of clustering algorithms such as K-Means, Bisecting K-Means and OPTICS to do clustering analysis for the early M-type stellar data in LAMOST survey. The performance of these algorithms on the early M-type stellar data is also discussed. In this paper, the performance of the Euclidean distance, the Manhattan distance, the residual distribution distance for the three clustering algorithms are studied, and the clustering algorithm depends on the distance measurement algorithm. The experimental results show that: (1) The clustering algorithm can well analyze the spectral data of the early M-type dwarf star, and the cluster data produced by clustering is very good with the MK classification. (2) The performance of the three different clustering algorithms is different, and Bisecting K-Means has more advantages in stellar spectral subdivision. (3) In the cluster at the same time it will produce some small number of clusters, and some rare celestial bodies can be found from these clusters. OPTICS is relatively suitable for finding rare objects.
WOS关键词	DATA RELEASE
WOS研究方向	Spectroscopy
语种	英语
出版者	OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
WOS记录号	WOS:000418728900045
内容类型	期刊论文
源URL	[http://ir.bao.ac.cn/handle/114a11/37341]
专题	中国科学院国家天文台
通讯作者	Pang Jing-chang
作者单位	1.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Peoples R China 2.Chinese Acad Sci, Key Lab Opt Astron, Natl Astron Observ, Beijing 100012, Peoples R China 3.Harbin Univ Sci & Technol, Rongcheng Campus, Weihai 264209, Peoples R China
推荐引用方式 GB/T 7714	Liu Jie,Pang Jing-chang,Wu Ming-lei,et al. Research of Clustering for LAMOST Early M Type Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2017,37(12):3904-3907.
APA	Liu Jie.,Pang Jing-chang.,Wu Ming-lei.,Liu Cong.,Wei Peng.,...&Liu Meng.(2017).Research of Clustering for LAMOST Early M Type Spectra.SPECTROSCOPY AND SPECTRAL ANALYSIS,37(12),3904-3907.
MLA	Liu Jie,et al."Research of Clustering for LAMOST Early M Type Spectra".SPECTROSCOPY AND SPECTRAL ANALYSIS 37.12(2017):3904-3907.