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A new method for clustering of boundary spectra
Journal of Astrophysics and Astronomy ( IF 1.1 ) Pub Date : 2020-06-06 , DOI: 10.1007/s12036-020-09634-x
Jianghui Cai , Yating Li , Haifeng Yang

The stellar spectral data taken by LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) include multiple types, some of which that fall between two spectral classes, namely boundary spectra. Due to the massive and high dimensional nature of spectra data, it will take a lot of time and energy to cluster these spectra by manual operation alone. To address this problem, a new clustering method based on influence space is presented in this paper. First, we introduce the concept of influence space to reduce the amount of data involved in the operation, and reduce the dimension of the data by extracting the main feature lines. Second, a novel method for initial cluster center selection is applied. Next, based on the selected initial cluster centres, other spectra are clustered by running K-means algorithm on the whole data set. The experimental results indicate that the initial cluster centres obtained by this method are of higher quality and the problem of boundary spectra clustering is also well solved.

中文翻译:

一种新的边界谱聚类方法

LAMOST(大天区多目标光纤光谱望远镜)拍摄的恒星光谱数据包括多种类型,其中一些属于两个光谱类别,即边界光谱。由于光谱数据的海量和高维性质,仅通过手动操作将这些光谱聚类将花费大量时间和精力。针对这一问题,本文提出了一种新的基于影响空间的聚类方法。首先,我们引入影响空间的概念来减少操作中涉及的数据量,通过提取主要特征线来降低数据的维度。其次,应用了一种用于初始聚类中心选择的新方法。接下来,基于选定的初始聚类中心,通过在整个数据集上运行 K-means 算法对其他光谱进行聚类。
更新日期:2020-06-06
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