聚類分析實驗報告

理學(xué)院數(shù)學(xué)與應(yīng)用數(shù)學(xué)系課程試驗匯報姓名班級應(yīng)數(shù)1101學(xué)號課程名稱多元記錄分析指導(dǎo)教師完畢日期.11.04試驗題目聚類分析試驗?zāi)康氖煜げ⒄莆蘸嫌糜跇悠肪垲惖南到y(tǒng)聚類法、動態(tài)聚類法以及對應(yīng)的CLUSTER過程和FASTCLUS過程；合用于變量聚類的VARCLUS過程。試驗內(nèi)容：1、下表是第五次全國人口普查中得到的我國內(nèi)地各省、市、自治區(qū)6歲以上人口受教育程度狀況分布(單位：人，資料來源：《中國記錄年鑒》)：規(guī)定：采用Ward最小方差聚類法進行聚類分析。（規(guī)定對原始數(shù)據(jù)原則化，不規(guī)定平方和為單位均方，且不用總離差平方和清除類間平房和得到平方半偏有關(guān)）用TREE過程繪制聚類過程圖。（聚為4類，按類降序排列，并分類打印輸出，其中識別變量仍為地區(qū)名）采用動態(tài)聚類法進行聚類分析。2、一組有關(guān)12盎司20種啤酒成分和價格的數(shù)據(jù)，分別用系統(tǒng)聚類法（距離用類平均距離）、動態(tài)聚類法，聚為4類，分別給出兩種聚類措施的分類成果及各類均值。啤酒名熱量（卡）鈉含量酒精含量價格Budweiser144194.70.43Schlitz181194.90.43Ionenbrau157154.90.48Kronensourc17075.20.73Heineken1521150.77Old-milnaukee145234.60.26Aucsberger175245.50.4Strchs-bohemi149274.70.42Miller-lite99104.30.43Sudeiser-lich11363.70.44Coors140164.60.44Coorslicht102154.10.46Michelos-lich135114.20.5Secrs150194.70.76Kkirin149650.79Pabst-extra-l68152.30.36Hamms136194.40.43Heilemans-old144244.90.43Olympia-gold-7262.90.46Schlite-light9774.20.47試驗過程與措施：第一題：程序：dataeducate;inputarea$illeterasaomangprimaryjuniorseniorzhuandazhuanbenkegraduate;labelarea='地區(qū)'illetera='未上過學(xué)'saomang='掃盲班'primary='小學(xué)'junior='初中'senior='高中'zhuan='中專'dazhuan='大專'benke='本科'graduate='碩士';cards;beijing5816374725523017264665166219728594607110299291082268172653tianjin5138215264124688913405530126146179284350654835542624040hebei37464611391599221350512609932353178931845716124121953983318020shanxi154336436723910319110126379842659168110342278407531454812452neimeng21866381660457261714811663223319108812476488912340876851liaoning2117529171572124825721675786339977871519869169244584565447251jilin134241618986289976479567968305714599719881329248697921600heilong1968874147023113282841411320038344721196355119969451784724013shanghai873696216143310663760385722665497111119693408378469876188jiangsu45855211638610240179942656247474327622120420185752194658458165zhejiang3212522133315016833356153194233848613110501395595548167930227anhui5937345155375222043367193676073084848143069592153942526417108fujian239082668730412875533114377322442143118645966418333162115927jiangxi21473847324571566057613455575294620410396647296943063528713shandong76175181515741294698113297345567504463195988204057292105439126henan54288581500232303217403591924769353462271789181713460073121656hubei4321859694959210818002039843551975802327881153744772958353623hunan2982232676936242199372259380451233011949147131474451171725229guangdo3897970764560282242733126142881819192836462204631190739177621guangxi191608457252918432526142184942787826142676475965027591811566hainan5818376803225986362455575707116238707167622683984343chongqi2173652373654132295598993338189798972604755597128966414440sichuan66083261664134353798262419376342680632029776137269262917536787guizhou525677872904315352997727482711375698591204648972057914178yunnan515780014304051897925490180441657168113144257687726369411988xizang10715341499697964421659574241850430232559962340shananxi266773060904412178915117077103307017106162491422453922232597gansu38810686704909280811601787618187276718744679591954499007qinghai10313331556771489401103523734258115020010910046396938ningxia65927019462517471411528464426233173640136967644381124xinjiang12275533026136997824508592714153588325557009962393036162;procprint;run;goptionsftext="宋體";procclusterdata=educatemethod=wardstdnonormpseudoouttree=educate1;varilleterasaomangprimaryjuniorseniorzhuandazhuanbenkegraduate;idarea;goptionsvsize=12hsize=8;proctreedata=educate1horizontalgraphicsn=4out=wardeducate1;copyareailleterasaomangprimaryjuniorseniorzhuandazhuanbenkegraduate;title'使用WARD法的譜系聚類圖';run;title'使用Ward法';procsortdata=wardeducate1;bycluster;run;procprintdata=wardeducate1;varclusterareailleterasaomangprimaryjuniorseniorzhuandazhuanbenkegraduate;run;Procstandarddata=educatemean=0std=1out=educate2;Varilleterasaomangprimaryjuniorseniorzhuandazhuanbenkegraduate;procfastclusdata=educate2out=educata3maxc=4list;varilleterasaomangprimaryjuniorseniorzhuandazhuanbenkegraduate;idarea;run;(1)ward法的并類歷史過程(1)由RSQ的變化狀況看，當分為4個類之前，并類過程中減少是逐漸的，變化不大，當分為4個類和三個類時差異較大，因此從看分為4個類較合適。(2)由SPRSQ（半偏）知，某步的半偏值越大．闡明上一步合并的效果好．此例中NCL＝1，2和3時較大，故分為二個類、三個類或四個類是較合適的．(3)偽F記錄量(列標題為PSF)越大表達這些觀測樣品可明顯地分為NCL個類。此例偽F最大和次大分別為NCL＝3，4和5(當NcL＜7)，闡明根據(jù)偽F準則分為二個類、三個類或四個類是較合適的．(4)偽記錄量值大表明上一次合并的兩個類是很分開的．也就是上一次聚類的效果是好的．此例偽最大和次大分別為NCL＝1和3，闡明根據(jù)偽準則分為二個類或四個類是較合適的.綜合以上可知，分為二類或四類較合適。使用ward法的譜系聚類圖ward法分為四類的成果成果：第一類：青海寧夏海南西藏第二類：福建江西內(nèi)蒙新疆廣西山西吉林重慶貴州甘肅黑龍江陜西天津安徽云南浙江第三類：河北湖南湖北江蘇河南山東四川廣東第四類：遼寧上海北京(2)動態(tài)聚類法：動態(tài)聚類的初始凝聚點（數(shù)據(jù)原則化）動態(tài)聚類的分類成果（數(shù)據(jù)原則化）成果：第一類：北京上海第二類:天津山西吉林內(nèi)蒙海南重慶西藏甘肅青海寧夏新疆第三類：河北、浙江、安徽、福建、江西、黑龍江、遼寧、湖南、廣西貴州云南陜西第四類：江蘇、山東、河南、湖北四川廣東第二題：程序：datapijiu;inputtype$x1-x4;cards;Budweiser 144 19 4.7 0.43Schlitz 181 19 4.9 0.43Ionenbrau 157 15 4.9 0.48Kronensourc 170 7 5.2 0.73Heineken 152 11 5 0.77Old-milnaukee 145 23 4.6 0.26Aucsberger 175 24 5.5 0.4Strchs-bohemi 149 27 4.7 0.42Miller-lite 99 10 4.3 0.43Sudeiser-lich 113 6 3.7 0.44Coors 140 16 4.6 0.44Coorslicht 102 15 4.1 0.46Michelos-lich 135 11 4.2 0.5Secrs 150 19 4.7 0.76Kkirin 149 6 5 0.79Pabst-extra-l 68 15 2.3 0.36Hamms 136 19 4.4 0.43Heilemans-old 144 24 4.9 0.43Olympia-gold- 72 6 2.9 0.46Schlite-light 97 7 4.2 0.47;procclusterdata=pijiumethod=avestdpseudorsqouttree=pijiu1;varx1-x4;idtype;goptionslsize=4hsize=8;/*控制輸出聚類圖的大小*/proctreedata=pijiu1horizontalgraphicsn=4out=avepijiu;copytypex1-x4;title'使用類平均法的譜系聚類圖';run;title'使用類平均法';procsortdata=avepijiu;bycluster;run;/*將聚類成果按類cluster進行排序*/procprintdata=avepijiu;run;procmeansdata=avepijiu;bycluster;varx1-x4;run;Procstandarddata=pijiumean=0std=1out=pijiu2;Varx1-x4;procfastclusdata=pijiu2out=pijiu3maxc=4list;varx1-x4;idtype;run;1:系統(tǒng)聚類法：類平均法的并類歷史過程(1)由RSQ的變化狀況看，當分為4個類之前，并類過程中減少是逐漸的，變化不大，當分為4個類和三個類時差異較大，因此從看分為4個類較合適。(2)由SPRSQ（半偏）知，某步的半偏值越大．闡明上一步合并的效果好．此例中NCL＝1，2和3時，較大，故分為兩個類、三個類或四個類是較合適的．(3)偽F記錄量(列標題為PSF)越大表達這些觀測樣品可明顯地分為NCL個類。此例偽F最大和次大分別為NCL＝4，5和6(當NcL＜7)，闡明根據(jù)偽F準則分為四個類、五個類或六個類是較合適的．(4)偽記錄量值大表明上一次合并的兩個類是很分開的．也就是上一次聚類的效果是好的．此例偽最大和次大分別為NCL＝2和3，闡明根據(jù)偽準則分為三個類或四個類是較合適的.綜合以上可知，分為四類較合適。類平均法分為四類的成果成果：第一類：BudweiserHammsCoorsStrchs-bohemiHeilemans-oldIonenbrauSchlite-lightOld-milnaukeeAucsberger第二類：Miller-liteSchlite-lightSudeiser-lichMichelos-lichCoorslicht第三類：KkirinKronensourcSecrs第四類：Pabst-extra-lOlympia-gold-提成4組后來，每組樣本的均值動態(tài)聚類法：動態(tài)聚類的初始凝聚點（數(shù)據(jù)原則化）動態(tài)聚類的分類成果（數(shù)據(jù)原則化）成果：第一類：Miller-liteSudeiser-lichSchlite-lightMichelos-lichCoorslicht第二類：BudweiserSchlitzIonenbrauOld-milnaukeeAucsbergerStrchs-bohemiCoorsHeilemans-oldHamms第三類：Olympia-gold-Pabst-extra-l第四類：KronensourcHeinekenSecrsKkirin每組樣本的均值：試驗成果分析(可附頁)：（在過程成果的下面）