title: [Automatic classification method of star spectrum data based on classification pattern tree].
abstract: Frequent pattern, frequently appearing in the data set, plays an important role in data mining. For the stellar spectrum classification tasks, a classification rule mining method based on classification pattern tree is presented on the basis of frequent pattern. The procedures can be shown as follows. Firstly, a new tree structure, i. e., classification pattern tree, is introduced based on the different frequencies of stellar spectral attributes in data base and its different importance used for classification. The related concepts and the construction method of classification pattern tree are also described in this paper. Then, the characteristics of the stellar spectrum are mapped to the classification pattern tree. Two modes of top-to-down and bottom-to-up are used to traverse the classification pattern tree and extract the classification rules. Meanwhile, the concept of pattern capability is introduced to adjust the number of classification rules and improve the construction efficiency of the classification pattern tree. Finally, the SDSS (the Sloan Digital Sky Survey) stellar spectral data provided by the National Astronomical Observatory are used to verify the accuracy of the method. The results show that a higher classification accuracy has been got.
authors.affiliation: School of Computer Science and Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China. 86672983@qq.com
authors.forename: Xu-Jun
authors.initials: XJ
authors.lastname: Zhao
authors.affiliation: School of Computer Science and Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China.
authors.forename: Jiang-Hui
authors.initials: JH
authors.lastname: Cai
authors.affiliation: School of Computer Science and Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China.
authors.forename: Ji-Fu
authors.initials: JF
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China.
authors.forename: Hai-Feng
authors.initials: HF
authors.lastname: Yang
authors.affiliation: School of Computer Science and Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Ma