APPLICATION AND RESEARCH OF DATA MINING BASED ON IMPROVED PCA METHOD

摘要：

The LAMOST (large sky area multi-object fiber spectroscopic telescope) is one of the national key scientific projects. It will yield 10,000-20,000 spectra per observation night. Automatic spectral analysis and recognition focused on helping astronomers finding their interesting celestial objects, become desirable and necessary. In this paper, an efficient data mining application based on improved Principal Component Analysis (PCA) is proposed, which has less computational complexity. Massive spectral data are clustered after dimension reduction by PCA and singular spectra candidate can be found out and identified by template.

关键词： PCA hierarchical clustering algorithms K-menas data mining

作者: Wen-Yu Wang Chuan-Xing Qu

作者单位: School of Information Engineering,Shandong University at Weihai Weihai, China Division of Research Administration Weihai vocational college Weihai, China

会议类型: 国际会议

会议名称: Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)

会议地点: 上海

会议语种:英文

页码: 140-143

在线出版日期: 2009-12-26（万方平台首次上网日期，不代表论文的发表时间）