华东理工大学2013—2014学年第二学期《多元统计分析与SPSS应用》实验报告5班级学号姓名开课学院商学院任课教师任飞成绩实验内容：实验5聚类分析方法1.熟悉SPSS中聚类分析的距离选择功能AnalyzeClassifyHierarchicalCluster在Measure栏中选择距离测度方法：Block---dij(1)Euclideandistance---dij(2)SquaredEuclideandistanceChebychev---dij(∞)Minkowski---dij(q)Cosine---cij(1)Pearsoncorrelation---cij(2)2.熟悉SPSS中聚类分析的系统聚类功能AnalyzeClassifyHierarchicalCluster在ClusterMethod栏中选择系统聚类方法：最短距离法(NearestLinkage)最长距离法(FurthestLinkage)中间距离法(MedianLinkage)重心法(Centroidclustering)类平均法(Between-groupsLinkage)(Within-groupsLinkage)离差平方和法(Ward’smethod)实验要求：1.选用例题5.1文件中的变量，完成系统聚类法的各种结果的比较分析例题5.1从21个工厂各抽一件同类产品，每个产品测两个质量指标，记作x1、x2，要求将各厂的产品按质量情况进行分类。12.对案例：Crop’Pain连锁店，展开聚类分析讨论思考：分类完成后，能进行哪些统计分析？改革二十多年来，中国经济的发展阶段划分的思考教师评语：教师签名：年月日实验报告：5.11、打开例题5.1.sav文件，，如图5.1.1所示依次Analyze→Classify→HierarchicalCluster,将x1，x2选入Variable框中图5.1.12、单击Statistics，选择ProximityMatrix，ClusterMembership中选择RangeofSolutions，依次输入6,12，如图5.1.2，单击Method，ClusterMethod为默认组间平均链锁法，单击Interval选项，激活右侧的参数框，单击下拉箭头，共有8个选项，选择Euclideandistance（欧式距离），如图5.1.3，单击Continue，单击Plots，选择Dendrogram，如图5.1.4单击Continue，选择OK得到结果图5.1.22图5.1.3图5.1.43图5.1.5距离矩阵ProximityMatrix.0001.0002.2363.6064.4725.0007.0717.2117.8109.2201.000.0002.0002.8284.1234.4726.4036.7087.2118.6022.2362.000.0002.0002.2362.8285.0005.0005.6577.0713.6062.8282.000.0002.2362.0003.6064.1234.4725.8314.4724.1232.2362.236.0001.0003.1622.8283.6065.0005.0004.4722.8282.0001.000.0002.2362.2362.8284.2437.0716.4035.0003.6063.1622.236.0001.4141.0002.2367.2116.7085.0004.1232.8282.2361.414.0001.0002.2367.8107.2115.6574.4723.6062.8281.0001.000.0001.4149.2208.6027.0715.8315.0004.2432.2362.2361.414.0005.0004.4726.3256.0008.0628.0009.22010.05010.19811.4024.4723.6065.0004.1236.3256.0837.0718.0008.0629.2205.0004.2435.8315.0997.2807.0718.0629.0009.05510.1986.7085.8317.0715.8318.0627.6168.0629.2209.05510.0006.4035.8317.6167.0719.2209.05510.05011.00011.04512.1665.0994.1234.1232.2364.2433.6064.0005.0995.0006.0837.0006.0006.3254.4726.4035.6575.3856.7086.3257.0718.0007.0007.2805.3857.2116.4035.8317.2116.7087.2807.0716.0836.7085.0007.0716.4036.3257.6167.2808.0629.0558.0628.5446.7088.6027.8107.2118.6028.0628.54411.40210.44011.1809.43411.40210.63010.00011.40210.81711.180Case12345678910111213141516171819202112345678910EuclideanDistanceThisisadissimilaritymatrix图5.1.6凝聚状态表4AgglomerationSchedule17191.00000612131.0000012891.000007561.0000013121.000001617181.207109781.207031011151.414001417201.88360157101.9627018342.000001312142.1182014352.3251141611122.4408121716173.1750917133.5925131811164.699141519175.29716102011215.817170201117.34918190Stage1234567891011121314151617181920Cluster1Cluster2ClusterCombinedCoefficientsCluster1Cluster2StageClusterFirstAppearsNextStage图5.1.7类成员聚类表ClusterMembership111111111111112222222332222244332224433222554433355443335544333654433376554448766544876654498765447655444109876551110987651110987651110987651110987651211109876Case12345678910111213141516171819202112Clusters11Clu...
