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Stellar spectra association rule mining method based on the weighted frequent pattern tree
Cai Jianghui1; Zhao Xujun1; Sun Shiwei2; Zhang Jifu1; Yang Haifeng1
2013
Source PublicationResearch in Astronomy and Astrophysics
ISSN1674-4527
Volume13Issue:3Pages:334
AbstractEffective extraction of data association rules can provide a reliable basis for classification of stellar spectra. The concept of stellar spectrum weighted itemsets and stellar spectrum weighted association rules are introduced, and the weight of a single property in the stellar spectrum is determined by information entropy. On that basis, a method is presented to mine the association rules of a stellar spectrum based on the weighted frequent pattern tree. Important properties of the spectral line are highlighted using this method. At the same time, the waveform of the whole spectrum is taken into account. The experimental results show that the data association rules of a stellar spectrum mined with this method are consistent with the main features of stellar spectral types.
Language英语
Funding Project[National Natural Science Foundation of China] ; [Shanxi Province Natural Science Foundation] ; [Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi, China] ; [Shanxi Province Science Foundation for Youths]
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/46481
Collection中国科学院国家天文台
Affiliation1.太原科技大学
2.中国科学院国家天文台
Recommended Citation
GB/T 7714
Cai Jianghui,Zhao Xujun,Sun Shiwei,et al. Stellar spectra association rule mining method based on the weighted frequent pattern tree[J]. Research in Astronomy and Astrophysics,2013,13(3):334.
APA Cai Jianghui,Zhao Xujun,Sun Shiwei,Zhang Jifu,&Yang Haifeng.(2013).Stellar spectra association rule mining method based on the weighted frequent pattern tree.Research in Astronomy and Astrophysics,13(3),334.
MLA Cai Jianghui,et al."Stellar spectra association rule mining method based on the weighted frequent pattern tree".Research in Astronomy and Astrophysics 13.3(2013):334.
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