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Clustering is an effective tool for astronomical spectral analysis to mine clustering patterns among data. With the implementation of large sky surveys, many clustering methods have been applied to effectively and automatically tackle spectroscopic and photometric data. Meanwhile, the performance of clustering methods under different data characteristics varies greatly. Aiming to summarize the astronomical spectral clustering algorithms and lay the foundation for further research, this paper gives a review of clustering methods on astronomical spectra data including next three parts. Firstly, lots of clustering methods on astronomical spectra are investigated and theoretically analysed appearing in algorithmic ideas, applications and features. Secondly, experiments are carried out on the unified datasets constructed by three criteria (spectra data type, spectra quality and data volume) to compare the performance of typical algorithms and the spectra data are selected from LAMOST and SDSS surveys. Finally, source codes of the comparison clustering algorithms and manuals for usage and improvement are provided on https://www.github.com/shichenhui/SpectraClustering.

The extensive growth of data quantity has posed many challenges to data analysis and retrieval. Noise and redundancy are typical representatives of the above-mentioned challenges, which may reduce the reliability of analysis and retrieval results and increase storage and computing overhead. To solve the above problems, A two-stage data pre-processing framework for noise identification and data reduction, called ARIS, is proposed in this paper. The first stage identifies and removes noises by the following steps: First, the influence space (IS) is introduced to elaborate data distribution. Second, a ranking factor (RF) is defined to describe the possibility that the points are regarded as noises, then, the definition of noise is given based on RF. Third, a clean dataset (CD) is obtained by removing noise from the original dataset. The second stage learns representative data and realizes data reduction. In this process, CD is divided into multiple small regions by IS. Then the reduced dataset is formed by collecting the representations of each region. The performance of ARIS are verified by experiments on artificial and real datasets. Experimental results show that ARIS effectively weakens the impact of noise and reduces the amount of data and significantly improves the accuracy of data analysis within a reasonable time cost range.

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