一种晚型天体光谱离群数据挖掘系统

探索海量的M型恒星中具有磁活动、巨星等较特殊、稀有的天体，对于后续观测、银河系结构、演化等科学研究具有重要的意义，针对M型恒星光谱特征线出现在子空间中的局部偏离，设计并实现了晚型恒星离群数据挖掘系统。首先采用稀疏因子和稀疏度系数度量样本在属性空间上的分布特征，并在此基础上对M型恒星光谱特征线进行离散化、降维等预处理，获得光谱子空间;然后采用微粒群算法搜索离群子空间，并证认子空间内光谱是否离群;此外，选择SDSS M型光谱特征线指数集为样本，实验分析了稀疏因子和稀疏度系数的设置对离群结果的影响，并将离群挖掘结果与SDSS提供光谱型等参数对照，表明利用该系统实现晚型恒星光谱特征线局部离群数据挖掘是可行并有价值的。

A Late-Type Star Spectra Outlier Data Mining System

In M star population, some special objects, which may be of magnetic activity, may be giant stars, or may be of other rare properties, are very important for the follow-up observation and the scientific research on galactic structure and evolution. For local bias of M-type star spectral characteristic lines contained in subspace, a late-type star spectra outlier data mining system is given in the present paper. Firstly, for the sample of M stellar spectral characteristic lines indices, its distribution characteristics in attribute spaces are measured by using the sparse factor and sparsity coefficient, and then this sample is discretized and dimension-reduced to the spectral subspace. Secondly, local outlier subspaces are extracted by PSO (particle swarm optimization) algorithm and identified. Additionally, the effects of sparse coefficient and sparse factor on the number of outliers are discussed by experiments on the sample of SDSS M stellar spectral line index set, and the outliers are compared with spectral type provided by SDSS. In this way, the feasibility and value of this system were validated.