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T he e ffic ien t analysis and processing o f astronom ical data is supported by In te rne t plus in teg ra tion in fo rm a­ tio n technology. T he massive observation data and secondary data have achieved e ffic ien t storage,re tr ie va l,d a ta analysis and in fo rm a tion m in ing. Combined w ith the fo u rth (D R4) released star spectral flow ed ca lib ra tion data of the large scien­ t if ic engineering L A M O S T telescope survey w h ich has owned C hina^s independent in te llec tua l p rope rty r ig h ts, tak ing the astronom ical data released by L A M O S T as an exam ple, R F IT S IO softw are package o f R language program m ing p la tfo rm is used to read and w rite the spectrum documents o f special F IT S fo rm a t. W ith the sta tis tica l data and data m in ing m ethod, the ve rifica tion scheme o f supervised g rid cluste ring was designed, by processing and iden tify in g the spectral data. T he spectral characteristics were extracted by dim ensiona lity reduction. T he characteristics o f the absorp­ tio n spectra were retained by norm alized continuous spectrum , then the center g rid o f cluste ring wave leng th scale was d iv ided ,and the s im ila rity measure func tion was used to describe the observed spectrum.

网格聚类分析天文光谱数据 (Analysis of Astronomical Spectral Data Based on Grid Clustering).