Outlier Data Mining and Analysis of LAMOST Stellar Spectra in Line Index Feature Space.

Large scale spectrum survey will produce mass spectral data and offer chances for searching rare and unknown types of spectra, which is contribute to revealing the evolution law of the universe and the origin of life. Data mining in outlier data in sky survey can serve the purpose of finding special spectra. Line index can be used in spectra data dimension reduction, keeping the spectral physical characteristics as much as possible, and at the same time, it can effectively solve the high dimensional spectral data clustering analysis in the high computation complexity. This paper proposed a method outlier data mining and analysis for massive stellar spectrum survey data based on line index characteristics, according to this, an outlier spectral data analysis method was proposed using line index characteristics space. Experimental results demonstrated that (1) using line index as the characteristic value of the spectrum can quickly perform the outlier data mining for high dimensional spectral data, and it can solve the problem of high computation complexity of the high dimensional spectral data. (2) this outlier data mining method was conducted based on the clustering results; it can effectively finding out emission stars, late type stars, late M type stars, extremely poor metal stars, and even finding spectra data missing certain data. (3) outlier data mining in line index feature space can help to analysis of rules of special stars found in the feature space. The mothed proposed in this paper based on the characteristics of line index outlier data mining and analysis method can be applied to the study of survey data.

[Research on the Clustering of Massive Stellar Spectra Based on Line Index].

Clustering algorithm is an important algorithm used to find the data distribution and implicit scheme in data mining. It can study spectra of large amount, multi-parameter and categories unknown simply and effectively. Using lick index as the eigenvalues of spectra can effectively improve the speed to calculate the high-dimensional spectra which can also retain more astrophysical characteristics of spectra. This paper finishes clustering of the survey data with k-means algorithm, using lick index as the eigenvalues of data with finished analysis results. The results show that the new method can gather data with similar physical characteristics together quicker and efficiently, with very good results in discovering rare stars. This method can be applied to the study of Survey data.