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.

Classification is valuable and necessary in spectral analysis, especially for data-driven mining. Along with the rapid development of spectral surveys, a variety of classification techniques have been successfully applied to astronomical data processing. However, it is difficult to select an appropriate classification method in practical scenarios due to the different algorithmic ideas and data characteristics. Here, we present the second work in the data mining series - a review of spectral classification techniques. This work also consists of three parts: a systematic overview of current literature, experimental analyses of commonly used classification algorithms and source codes used in this paper. Firstly, we carefully investigate the current classification methods in astronomical literature and organize these methods into ten types based on their algorithmic ideas. For each type of algorithm, the analysis is organized from the following three perspectives. 1) their current applications and usage frequencies in spectral classification are summarized; 2) their basic ideas are introduced and preliminarily analysed; 3) the advantages and caveats of each type of algorithm are discussed. Secondly, the classification performance of different algorithms on the unified datasets is analysed. Experimental data are selected from the LAMOST survey and SDSS survey. Six groups of spectral datasets are designed from data characteristics, data qualities and data volumes to examine the performance of these algorithms. Then the scores of nine basic algorithms are shown and discussed in the experimental analysis. Finally, nine basic algorithms source codes written in python and manuals for usage and improvement are provided.