摘要: 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
data sets is analysed. Experimental data are selected from the LAMOST survey
and SDSS survey. Six groups of spectral data sets 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.
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