#########################################0808聚类分析 X<-data.frame( x1=c(2959.19, 2459.77, 1495.63, 1046.33, 1303.97, 1730.84, 1561.86, 1410.11, 3712.31, 2207.58, 2629.16, 1844.78, 2709.46, 1563.78, 1675.75, 1427.65, 1783.43, 1942.23, 3055.17, 2033.87, 2057.86, 2303.29, 1974.28, 1673.82, 2194.25, 2646.61, 1472.95, 1525.57, 1654.69, 1375.46, 1608.82), x2=c(730.79, 495.47, 515.90, 477.77, 524.29, 553.90, 492.42, 510.71, 550.74, 449.37, 557.32, 430.29, 428.11, 303.65, 613.32, 431.79, 511.88, 512.27, 353.23, 300.82, 186.44, 589.99, 507.76, 437.75, 537.01, 839.70, 390.89, 472.98, 437.77, 480.99, 536.05), x3=c(749.41, 697.33, 362.37, 290.15, 254.83, 246.91, 200.49, 211.88, 893.37, 572.40, 689.73, 271.28, 334.12, 233.81, 550.71, 288.55, 282.84, 401.39, 564.56, 338.65, 202.72, 516.21, 344.79, 461.61, 369.07, 204.44, 447.95, 328.90, 258.78, 273.84, 432.46), x4=c(513.34, 302.87, 285.32, 208.57, 192.17, 279.81, 218.36, 277.11, 346.93, 211.92, 435.69, 126.33, 160.77, 107.90, 219.79, 208.14, 201.01, 206.06, 356.27, 157.78, 171.79, 236.55, 203.21, 153.32, 249.54, 209.11, 259.51, 219.86, 303.00, 317.32, 235.82), x5=c(467.87, 284.19, 272.95, 201.50, 249.81, 239.18, 220.69, 224.65, 527.00, 302.09, 514.66, 250.56, 405.14, 209.70, 272.59, 217.00, 237.60, 321.29, 811.88, 329.06, 329.65, 403.92, 240.24, 254.66, 290.84, 379.30, 230.61, 206.65, 244.93, 251.08, 250.28), x6=c(1141.82, 735.97, 540.58, 414.72, 463.09, 445.20, 459.62, 376.82, 1034.98, 585.23, 795.87, 513.18, 461.67, 393.99, 599.43, 337.76, 617.74, 697.22, 873.06, 621.74, 477.17, 730.05, 575.10, 445.59, 561.91, 371.04, 490.90, 449.69, 479.53, 424.75, 541.30), x7=c(478.42, 570.84, 364.91, 281.84, 287.87, 330.24, 360.48, 317.61, 720.33, 429.77, 575.76, 314.00, 535.13, 509.39, 371.62, 421.31, 523.52, 492.60, 1082.82, 587.02, 312.93, 438.41, 430.36, 346.11, 407.70, 269.59, 469.10, 249.66, 288.56, 228.73, 344.85), x8=c(457.64, 305.08, 188.63, 212.10, 192.96, 163.86, 147.76, 152.85, 462.03, 252.54, 323.36, 151.39, 232.29, 160.12, 211.84, 165.32, 182.52, 226.45, 420.81, 218.27, 279.19, 225.80, 223.46, 191.48, 330.95, 389.33, 191.34, 228.19, 236.51, 195.93, 214.40), row.names = c("北京bj", "天津tj", "河北hb", "山西sx", "内蒙古nmg", "辽宁ln", "吉林jl", "黑龙江hlj", "上海sh", "江苏sz", "浙江zj", "安徽ah", "福建fj", "江西jx", "山东sd", "河南hn", "湖北hb", "湖南hun", "广东gd", "广西gx", "海南hain", "重庆cq", "四川xc", "贵州gz", "云南yn", "西藏xz", "陕西sx", "甘肃gs", "青海qh", "宁夏nx", "新疆xj") ) Province<-dist(scale(X))#先标准化，然后计算距离 hc1<-hclust(Province, "complete") hc2<-hclust(Province, "average") hc3<-hclust(Province, "centroid") hc4<-hclust(Province, "ward.D") opar<-par(mfrow=c(2,1), mar=c(5.2,4,0,0))#于下面的par()对应，画图用 plot(hc1,hang=-1) re1<-rect.hclust(hc1,k=5,border="red") plot(hc2,hang=-1) re2<-rect.hclust(hc2,k=5,border="red") par(opar) opar<-par(mfrow=c(2,1), mar=c(5.2,4,0,0)) plot(hc3,hang=-1) re3<-rect.hclust(hc3,k=5,border="red") plot(hc4,hang=-1) re4<-rect.hclust(hc4,k=5,border="red") par(opar) #####动态聚类法 km=kmeans(scale(X),5,nstart = 20);km
