|
|
|
|
|
|
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 |
1. School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Weihai 264209, China
2. Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
3. Harbin University of Science and Technology Rongcheng Campus, Weihai 264209, China |
|
|
Abstract 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.
|
Received: 2017-01-01
Accepted: 2017-04-28
|
|
Corresponding Authors:
PANG Jing-chang
E-mail: pjc@sdu.edu.cn
|
|
[1] Luo A L, Zhang H T, Zhao Y H, et al. Research in Astronomy and Astrophysics, 2012, 12(9): 1243.
[2] Luo A L, Zhao Y H, Zhao G, et al. Research in Astronomy and Astrophysics, 2015, 15(8): 1095.
[3] Hartigan J A, Wong M A. Journal of the Royal Statistical Society, Series(Applied Statistics), 1979,28(1): 100.
[4] Kanungo T, Mount D M, Netanyahu N S, et al. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2002, 24(7): 881.
[5] Ankerst M, Breunig M M, Kriegel H P, et al. OPTICS: Ordering Points to Identify the Clustering Structure. ACM Sigmod Record. ACM, 1999, 28(2): 49.
[6] Almeida J S, Prieto C A. The Astrophysical Journal, 2013, 763(1): 50.
[7] LIU Jie, PAN Jing-chang, LUO A-li, et al(刘 杰, 潘景昌, 罗阿理, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(12): 3524.
[8] Kong X, Cheng F Z. Monthly Notices of the Royal Astronomical Society, 2001, 323(4): 1035.
[9] Wei P, Luo A, Li Y, et al. The Astronomical Journal, 2014, 147(5): 101. |
[1] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
[2] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
[3] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[4] |
XU Rong1, AO Dong-mei2*, LI Man-tian1, 2, LIU Sai1, GUO Kun1, HU Ying2, YANG Chun-mei2, XU Chang-qing1. Study on Traditional Chinese Medicine of Lonicera L. Based on Infrared Spectroscopy and Cluster Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3518-3523. |
[5] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[6] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[7] |
LI Xin-li1, CONG Li-li2, XU Shu-ping2, LI Su-yi1*. Cell Growth Analysis Method Based on Spectral Clustering and Single-Cell Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2832-2836. |
[8] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
[9] |
LI Shuang-chuan, TU Liang-ping*, LI Xin, WANG Li-li. Besvm: A-Type Star Spectral Subtype Classification Algorithm Based on Transformer Feature Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1575-1581. |
[10] |
LIU Si-qi1, FENG Guo-hong1*, TANG Jie2, REN Jia-qi1. Research on Identification of Wood Species by Mid-Infrared Spectroscopy Based on CA-SDP-DenseNet[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 814-822. |
[11] |
LI Zi-yi1, LI Rui-lan1, LI Can-lin1, WANG Ke-ru2, FAN Jiu-yu3, GU Rui1*. Identification of Tibetan Medicine Zhaxun by Infrared Spectroscopy
Combined With Chemometrics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 526-532. |
[12] |
LI Xiao1, CHEN Yong2, MEI Wu-jun3*, WU Xiao-hong2*, FENG Ya-jie1, WU Bin4. Classification of Tea Varieties Using Fuzzy Covariance Learning
Vector Quantization[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 638-643. |
[13] |
CHEN Yong1, 2, GUO Yun-zhu1, WANG Wei3*, WU Xiao-hong1, 2*, JIA Hong-wen4, WU Bin4. Clustering Analysis of FTIR Spectra Using Fuzzy K-Harmonic-Kohonen Clustering Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 268-272. |
[14] |
YUAN Zhuang1, DONG Da-ming2*. Near-Infrared Spectroscopy Measurement of Contrastive Variational Autoencoder and Its Application in the Detection of Liquid Sample[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3637-3641. |
[15] |
SHANG Chao-nan1, XIE Yan-li2, GAO Xiao3, ZHOU Xue-qing2, ZHAO Zhen-dong2, MA Jia-xin1, CUI Peng3, WEI Xiao-xiao3, FENG Yu-hong1, 2*, ZHANG Ming-nan2*. Research on Qualitative and Quantitative Analysis of PE and EVA in Biodegradable Materials by FTIR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3380-3386. |
|
|
|
|