Title :
A Data Mining Application in Stellar Spectra
Author :
Jiang Bin ; Pan Jing Chang ; Yi Zhen Ping ; Guo Qiang
Author_Institution :
Sch. of Inf. Eng., Shandong Univ. at Weihai, Weihai, China
Abstract :
The current practice of recognition spectra manually is no longer applicable to a large extent. This work is particularly focused on helping astronomers finding their interesting celestial objects. In this paper an efficient hierarchical clustering data mining method based on principal component analysis (PCA) is proposed. Massive stellar spectral data are clustered by improved hierarchical clustering method after dimensionality reduction by PCA.The singular points are found out after definition according to experience. An application implemented in the automated spectral analysis system based on the method is carried out and some significative data are found out.
Keywords :
astronomy computing; data mining; pattern clustering; principal component analysis; stellar spectra; PCA; astronomers; automated spectral analysis system; dimensionality reduction; hierarchical clustering data mining method; principal component analysis; stellar spectra; Application software; Clustering methods; Computer science; Data engineering; Data mining; Eigenvalues and eigenfunctions; Equations; Principal component analysis; Space technology; Spectral analysis; PCA; data mining; dimensional data; hierarchical clustering method;
Conference_Titel :
Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3746-7
DOI :
10.1109/ISCSCT.2008.121
