采用信息熵思想, 给出一种基于属性权值和wk-距离的异常天体光谱特征线挖掘方法, 并开发了天体光谱异常特征线挖掘系统。 首先采用信息熵思想计算天体光谱特征线属性权值, 从而有效地刻画每条特征线的重要程度; 其次采用邻域半径的剪枝技术, 对海量天体光谱特征线数据集约简, 删除不可能成为异常的数据对象, 形成一个候选异常数据集; 然后根据离候选异常数据中对象之间的偏差, 计算wk-距离和, 并选取wk-距离和较大的前TOP-NN个数据对象作为天文光谱异常特征线数据; 最后采用SDSS恒星光谱特征线数据集, 实验和系统运行结果验证了该方法的有效性和可行性。

Outlier mining is one of the effective methods to find the abnormal celestial spectrum data, and is also one of effective ways to discover the special and unknown celestial bodies. In the present paper, an abnormal characteristic line mining method of celestial spectrum is presented based on the attribute weight and wk-distance by using the idea of information entropy. Based on it, an abnormal characteristic line mining system of celestial spectrum was designed and implemented. Firstly, attribute weight of characteristic line was determined by using the idea of information entropy, so that important degree was effectively reflected for each characteristic line. Secondly, massive characteristic line data set of celestial spectrum was reduced by utilizing pruning technique based on neighborhood radius, so that candidate set of abnormal characteristic line was obtained by deleting data objects in which there may not be abnormal characteristic lines. Thirdly, wk-distance sum was computed according to the deviation between the data objects in the candidate set, and the objects whose wk-distance sum value ranks the first top n were regarded as abnormal characteristic line data objects. In the end, the experimental and the system’s running results validated the effectiveness and feasibility of the method by using the SDSS star spectral data set.