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Research of Clustering for LAMOST Early M Type Spectra
Liu Jie1; Pang Jing-chang1; Wu Ming-lei1,3; Liu Cong1; Wei Peng2; Yi Zhen-ping1; Liu Meng1
2017-12-01
Source PublicationSPECTROSCOPY AND SPECTRAL ANALYSIS
ISSN1000-0593
Volume37Issue:12Pages:3904-3907
AbstractLarge-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.
KeywordLAMOST Clustering Dimension reduction
DOI10.3964/j.issn.1000-0593(2017)12-3904-04
WOS KeywordDATA RELEASE
Language英语
WOS Research AreaSpectroscopy
WOS SubjectSpectroscopy
WOS IDWOS:000418728900045
PublisherOFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/37342
Collection中国科学院国家天文台
Corresponding AuthorPang Jing-chang
Affiliation1.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
Recommended Citation
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.
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