数据仓库与数据挖掘实验报告

数据仓库与数据挖掘

实验报告题目Glass（玻璃）数据集分析TOC\o"1-5"\h\z院系姓名学号专业班级科目数据仓库与数据挖掘任课老师一、实验目的二、实验内容数据预处理方法（缺失值处理）数据可视化分类算法测试及比较三、实验步骤Weka平台搭建及收集该数据集加载Glass（玻璃）数据集数据预处理数据可视化分类算法四、实验总结一、实验目的使用Weka数据预处理方法，对缺失值数据进行处理。缺失值会使数据挖掘混乱，分析可能会得到错误结论，所以在数据挖掘前最好进行缺失值数据进行处理。使用数据挖掘中的分类算法，对数据集进行分类训练并测试，应用不同的分类算法，比较他们之间的不同。学习与了解Weka平台的基本功能与使用方法。二、实验内容数据预处理方法（缺失值处理）数据可视化分类算法测试及比较三、实验步骤Weka平台搭建及收集该数据集加载Glass（玻璃）数据集（1）Glass（玻璃）数据集预处理界面如图2.1所示::M叩g我共关联迭择医性可现化I.:::序号buildTiadfLo«.t计教蹲7Ci02buil-1tiheIXL3...ftTE.gLT1704v&kie73adEL>3..Q005iaisa弟CBtle'etre390Llivdluapa292&.0图2.1Glass（玻璃）数据集预处理界面图示（2）Glass（玻璃）数据属性含义如表2.1所示:RI相对原子质量Na钠Mg镁Al铝Si硅K钾Ca钙Ba钡Fe铁Type类型表2.1Glass（玻璃）数据属性含义数据预处理（1）Glass（玻璃）数据预处理前数据如图3.1所示：圄ViewerXReLati_on：GlassNo.1:KEHun]=»ric2：Ua}™n=»ric3:Mg4:Al5:Siic-6:K7：Ca8：BaIransric9：FeJiuHisric-10：TypeNzsiinal11.5179312.793.51.1273.D30.648.770.00.0buiLit...21.5164312.163.521.3572.明0.578.530.00.0vehict...31.5179313.213.431.4L72.640.533.430.00.0build,n...41.51299L4.41.741.5474.557.590.0O.QtMble甘are51.5339312.31.070.160.1216-190.00.24build,t...61.5165512.752.851.4473.270.578.790.110.22build,w...了1.5177913.643.65Ci.6573.00.06B.930.00.0vehict...81.5183713.142.S41.2872.S50.559.070.00.0build,w...91.5154514.140.02.6373.390.089.070.610.05headlamps101.5178972.330.558.440.00.28Lui11T...1115162513.353.5S1.4972.720.45S.210.00.0Lnild.w...121.51743673.550.62S.90.00.24Lnild.t...1315222313.213.77a7971.990.1310.020.00.0buiLit...141.6212114.033.76a.ss71.790.119.650.00.0vehiet...151.5166513.143.451.7572.430.68.380.17vehict...161.5170713.433.4S1.7172.520.627.990.0O.Qbuild,t...171.5171914.750.02.073.020.08.531.590.08headlampeIS1.5162912.713.331.4973.2S0.67S.240.00.0build,w...191.5199413.270.01.7673.030.4711.320.00.0containers201.51S1112.962.961.4372.920.68.790.140.0build,t...211.5215213.053.650.8750.00.17build,t...221.5247511.450.0L.8S72.190.8113.240.00.34build,w...231.51S4112.933.741.1172.2S0.64S.960.00.22Lnild.t...241.5175413.393.661.1972.790.570.270.00.11Lnild.w...25L5206E12.而1.6121772.180.71eonta-infetE261.5156913.243.431.4T73.250.338.030.00.0build,t...271.515912.323.521.972.S60.637.970.00.0build,t...281.5168314.560.0L.9S73.290.08.521.570.07headlampe2915168713.233.541.4S72.840.568.10.00.0build,w...301.516113.333.531.3472.670.568.330.00.0vehict...UndoOKCaneel

图3.1Glass（玻璃）数据预处理前数据图示（2）使用缺失值处理函数：weka.filters.unsupervised.attribute.ReplaceMissingValue&Glass（玻璃）数据预处理后数据如图3.2所示：I另11on:G1义£flltEES.unwxipQifTis色d.adz比ibute.EiplsiBMi曙ingFsLlg§No.1:FUUtifflaric2:1UBirnsric3:MgIwnsriE4:ALBuoarinc5:SiBunsric6:EIfcinieric7：CaHhinisric8：BaBirnsrir9：FeMm过210:TypeIfccniMl115179312.753.51.L273.030.640.770.00.0buildif...21.5164312.L$3.521.35TN.390.573.530.00.0vehigf...315179313.213.4S1.4175.540.ESS.430.00.0Lui1dt...41.5129914.41741.S474.ESQ.O7.E90.00.0tablevare61.5339312.30.01.070.160.1216.190.00.24Lui1dt...615165512.752.351.4473.270.578.790.110.22buildt...715177313.643.650.0573.0o.oe8.930.00.0vehicr...s151B3713.142.841.2372.B50.559.070.00.0buildw...91.5154514.LI0.02.0S73.390.QB9.070.61O.'J5heallamps101.5175572.330.5SS.440.00.23buiLdr...111.5162513.363.551.4972.72O.46S.210.0O.0buildf...1215174312.23.251.L673.EE0.62S.90.00.24hulldf...1315322313.213.770.7S71.990.1310.020.00.0huildf...14152L2114.033.7E0.5S71.790.119.650.00.0vekiof...1515166513.143.451.7672.4S0.68.380.00.17vehicr...1615170713.483.481.7172.520.627.990.00.0buildw...171.5171314.750.02.073.02Q.OS.53159O.'JSheallampsIS1.51泌12.713.331.1973.2E0.67B.210.00.0buiLd审，,.1915199413.270.01.T673.03O.4711.320.0O.0containers2015151112.962.窕1.4372.S20.6S.79Cl.140.0LulldT...2115215213.053.650.3750.00.L?Lui1dt...2215247E11.4E0.0i.as72.190.SI13.240.00.34Lui1dt...23151S4112.933.741.1172.280.64B96Cl.Cl0.22buildt...2415175413.393.661.L972.790.578.27Cl.Cl0.11buildt...2515205S12.S51.612.1772.180.71contairiere2615156513.24r3.491一甘73.250.33S.030.00.0build*■一271..515S12.323.521.972.56Q.6S7.970.00.0buildf...2S1516S314.550.01.9S73.290.0S.B21570.07kead.1snips291SIGHT13.233.541.4S72S40.E63.10.00.0buildr...301..516113.333.531.3472.670.E6S.330.00.0vekicr...UndoOKCiscncel图3.2Glass（玻璃）数据预处理后数据图示

数据可视化Glass（玻璃）数据可视化如图4.1所示:图4.1Glass（玻璃）数据可视化图示5.分类算法(1)KNN算法：一种统计分类器，对数据的特征变量的筛选尤其有效。KNN算法如图5.1所示:图5.1KNN算法图示①元素分析结果如表5.1所示:化学元素相关系数平均绝对误差平均方根误差相对绝对误差相对平方根误差实例总数RI0.74570.00120.002155.0903%68.5252%214Na0.72670.38590.585763.9212%71.38%214Mg0.89190.30930.665225.4706%46.0347%214Al0.71130.233250.367464.432%73.3956%214Si0.55930.41240.671274.0605%86.6187%214K0.83580.12760.404843.2052%62.0426%214Ca0.87440.38180.702741.4808%49.3278%214Ba0.56170.12860.476443.8101%95.771%214Fe0.07710.7780.132699.8531%135.3508%214表5.1元素分析结果②类型分析结果：准确率为70.5607%，其中214个实例数据有151个正确分类，63个错误分类。根据混淆矩阵，被错误分类的实例如图5.2：abcde-0<--classifiedam555W0000|日=buiIdwind^'1cat.15514□321I1k=14wind33500□0|1c=veiiicwindIcat.00□00□0I1d=vehicwindncm-^loat;02□010□111e=containers0100170I1三=tabl-eware030Cl2123|勺=headlanipsCon^uaionMatrix图5.2错误分类的实例图示

(2)J48决策树算法：采用贪婪和自上而下的决策树方法。对决策树采取了贪婪和自上而下的方法。J48决策树算法如图5.3所示:153153Dni115l=hulldvin-dELcttt47iD2321。=hulldvin-dhe-jl-flcat5€D01ai-=vehxcvi-Zrdflc-B-t00D00aid=vehxcvi-Zrdhe-jl-fIcat20010aiis.=.cnntaiDers100a7aIE二tatlewiire"口Striitifi-sdcec-sa-vaSmsinirv—S.S.CorrectlyCle-ssifi=dIzutazices142砧-醛里tIncorrecTlyClasallied.Instances7133.L77CfFfappa3TiE.TX3E1CD.55HeanabsoLiiu?■?trueD,103€Foot;Tiffintrrcr0,2097Relative汕soLu匚肥error40,45071Foot;reLarivra-juncederr>iB9.27371CortTragtofccsa^s(0,3570,9721relrre-gl^nsize[0,3-5level]31.42B€*TotalPuBberDiInat^nces314—DerallEdAcciLE-ac-a'By"lass—TPRateIT财姑Pr-ecLeii>:iiP.eMllF-PfeegreMCCR0CFRCArea€laa£i0.1740■667d.1140.690D.532a.3060.667buildwind.flOdlO.£l：30.1&L0653d.£150.635D.44Jd.7650.606buildWlZld.ZlOQ-floaL0.04€04DDd.3530.375D.325a.7660.251VEhiGWlZld.flOdlo.aaa0.0000odda.aoo0.000D.Dnn□VEhiGWlZld.ZlOQ-floaLLIES0.0100S33CLM90.800D.7SSd.3720.575ccjicainerB0../7130-029053B0-1730.636D.635a-5300u5270../75-30-0220£52CLM30-821D.755a-3-fi?OuTSS址MiaapmvTeigbzed螫必O.EES0-1300€7DQ-E-fiS0.668D.535a-307OufiLL图5.3J48决策树算法图示结果分析：准确率为66.8224%，其中214个实例数据有143个正确分类，71个错误分类。根据混淆矩阵，被错误分类的实例如图5.4：ConfusionMatrixa.bcde1gc--classified招50153□01i1a=buildwiri-d^loatLn47S0232|b=buildwin-dnon-float55■600101g=vehicwin<i-1Q^000□00□1d=vehlcwind020□10011e=contaiELers110□07□11=tableware320□01231g=tiEadlamps图5.4错误分类的实例图示

（3）贝叶斯算法：非常简单快速的分类算法，适用于维度非常高的数据集。贝叶斯算法如图5.5所示:=Stratifi-edcra-aa-valldat-iori===CnrrectlyCleaaitiedInstancrs104ae.5S3i*Incorre-ctlyClHaaifledInstE-nces11051.40191Efappaatiitistit:0.31CSMeanehsolut*ceec-e0.1S41BootsjnnECilerrorReliarLvedCsolnteerror72,7766句Rmtrtlaulvracniaieilerroi104.701%CoTera^enfcases[0,95Le^LjT0.Q!935上Nfeainrel^Kalse[U-95Level?Z5.S01Z/ToralUuifbET<?fInsMnces314—DeLdllE-lAGCUTiGYByGldL88—IFaateITEaceP^clsionRjecaLLE-催如皿：MCCROCiTriPRCAxedClass0-725Ou4240.4550u729D.5€0D.70H0.45StuLL3WlmlflMT；Qu271OuLOL0.4310u171D.2520u100D.7170u50€buiLdwind^oa-floatCL：235O.Oef0.2500u335D.2110u135D.7230u2£2windfloatQu0000.000o.taaaOuoooD.QOO0jOOu??-p'ehi-Ewind.zioa-fMat;a.1030.04-0L：333a.aoeD..32O0.278D-Efll口-35containe-ja.3390-02-9fl.511(LB的O..E&€0.6980.965a.T2Clttbl^waz-sa.8280-0220.S51Q.S2SD..S420.8LSD..92Q口-7的阮31呵AWeightedAvg.