Abstract To search the unknown laws of celestial bodies is one of the objectives of human exploration of the universe. Utilizing the association rules is an effective way to find out the inherent and unknown interrelationships between characteristics of the celestial spectrum data and its physical and chemical properties. Using the national science project LAMOST as application background, constrained FP tree and its constructing algorithm are presented by taking first order predicate logic as knowledge representation technique of celestial spectrum data. Consequently, the interrelation analysis efficiency and the pertinence of celestial spectrum data are greatly improved. Thus, an interrelation analysis method of celestial spectrum data is proposed. The experimental results validate that the proposed method is feasible and valuable.
[1] Luo Ali. Pattern Recognition Methods in Automatic Technique for Astronomical Spectral Analysis. Ph.D Dissertation. Beijing, China: Chinese Academy of Sciences. National Astronomical Observatories, 2001 (in Chinese) (罗阿理.光谱自动分析技术中的模式识别方法研究.博士学位论文.北京:中国科学院国家天文台, 2001) [2] Liu Zhongtian. Automatic Recognition and Classification of Stellar Spectra. Ph.D Dissertation. Beijing, China: Chinese Academy of Sciences. Institute of Automation, 2006 (in Chinese) (刘中田.恒星光谱的自动识别与分类方法研究.博士学位论文.北京:中国科学院自动化研究所, 2006) [3] Luo Ali, Zhao Yongheng. Astronomical Spectral Lines Auto-Searching Using Wavelet Technology. Chinese Journal of Astronomy and Astrophysics, 2000, 20(4): 427-437 [4] Liu Rong, Duan Fuqing, Liu Sanyang, et al. Spectral Classification of Galaxy Based on Wavelet Feature. Acta Electronica Sinica, 2005, 33(11): 2059-2062 (in Chinese) (刘 容,段福庆,刘三阳,等.基于小波特征的星系光谱分类.电子学报, 2005, 33(11): 2059-2062) [5] Tan Dongmei, Hu Zhanyi, Zhao Yonghen. A PCA Based Efficient Stellar Spectra Classification Method. Spectroscopy and Spectral Analysis, 2003, 23(1): 182-186 (in Chinese) (覃冬梅,胡占义,赵永恒.一种基于主分量分析的恒星光谱快速分类法.光谱学与光谱分析, 2003, 23(1): 182-186) [6] Liu Zhongtian, Li Xiangru, Wu Fuchao, et al. A Method for Auto-Recognizing the M-Type Stars Based on Wavelet Feature. Acta Electronica Sinica, 2007, 35(1): 157-160 (in Chinese) (刘中田,李乡儒,吴福朝,等.基于小波特征的M型星自动识别方法.电子学报, 2007, 35(1): 157-160) [7] Yang Jinfu, Wu Fuchao, Luo ALi, et al. Automated Classification for Celestial Spectra Based on Cover Algorithm. Pattern Recognition and Artificial Intelligence, 2006, 19(3): 368-374 (in Chinese) (杨金福,吴福朝,罗阿理,等.基于覆盖算法的天体光谱自动分类. 模式识别与人工智能, 2006, 19(3): 368-374) [8] Zhang Jifu, Cai Jianghui. A Study on the Outlier Mining System for LAMOST Spectra. Spectroscopy and Spectral Analysis, 2007, 27(3): 606-609 (in Chinese) (张继福,蔡江辉.面向LAMOST的天体光谱离群数据挖掘系统研究.光谱学与光谱分析, 2007, 27(3): 606-609) [9] Zhang Jifu, Jiang Yiyong, Hu Lihua, et al. A Concept Lattice Based Recognition Method of Celestial Spectra Outliers. Acta Automatica Sinica, 2008, 34(9): 1060-1066 (in Chinese) (张继福,蒋义勇,胡立华,等.基于概念格的天体光谱离群数据识别方法.自动化学报, 2008, 34(9): 1060-1066) [10] Agrawal R, Imielinski T, Swami A. Mining Association Rules between Sets of Items in Large Databases // Proc of the 1st International Conference on Management of Data. Washington, USA, 1993: 207-216 [11] Han Jiawei, Pei Jian, Yin Yiwen, et al. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery, 2004, 8(1): 53-87 [12] Pei Jian, Wang Haixun, Liu Jian, et al. Discovering Frequent Closed Partial Orders from Strings. IEEE Trans on Knowledge and Data Engineering, 2006, 18(11): 1467-1481 [13] Chen Anlong, Tang Changjie, Tao Hongcai, et al. An Improved Algorithm Based on Maximum Clique and FP-Tree for Mining Association Rules. Journal of Software, 2004, 15(8): 1198-1207 (in Chinese) (陈安龙,唐常杰,陶宏才,等.基于极大团和FP-Tree的挖掘关联规则的改进算法.软件学报, 2004, 15(8): 1198-1207) [14] Huang T S. Two-Dimensional Digital Signal Processing Ⅱ: Transforms and Median Filters. New York, USA: Springer-Verlag, 1981 [15] Wang Yongqing. Artificial Intelligent Theory and Method. Xi'an, China: Xi'an Jiaotong University Press, 2003 (in Chinese) (王永庆.人工智能原理与方法.西安:西安交通大学出版社, 2003)
2009, Vol. 22 
