# 1、数据准备
# 1、数据准备
tt<-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","天津","河北","山西","内蒙古",
"辽宁","吉林","黑龙江","上海","江苏","浙江",
"安徽","福建","江西","山东","河南","湖北",
"湖南","广东","广西","海南","重庆","四川",
"贵州","云南","西藏","陕西","甘肃","青海" ,
"宁夏","新疆")
)
# 2、dist()将数据转化为两点之间的距离
d<- dist(scale(tt),method = 'euclidean') #scale对数据做中心化或者标准化处理 # 注释：在聚类中求两点的距离有： # 1）绝对距离：manhattan # 2）欧式距离：euclidean 默认 # 3）闵可夫斯基距离：minkowski # 4）切比雪夫距离：chebyshev # 5）马氏距离：mahalanobis # 6）蓝氏距离：canberra
# 3、代入两点距离(d)，method='ave'指使用类平均法聚类
hc1 <- hclust(d,method='average') #hclust提供系统聚类的计算 最长距离法 # 注释：聚类中集合之间的距离： # 1）类平均法：average # 2）重心法：centroid # 3）中间距离法：median # 4）最长距离法：comolete 默认 # 5）离差平方和法：ward # 6）密度估计法：density
# 4、聚类视图
# 方法1 plclust(hc1, hang=-1,labels=row.names(tt)) #hang是表明谱系图中各类所在的位置 当hang取负值时，谱系图中的类从底部画起 # 方法2 plot(hc1,hang=-1,labels=row.names(tt)) #hang是表明谱系图中各类所在的位置 当hang取负值时，谱系图中的类从底部画起 rect.hclust(hc1, k=5, border="red")
# 5、生成结果及展示
groups <- cutree(hc1,k=5) result<-cbind(tt,groups) result[order(result$groups),]来源： https://www.cnblogs.com/liuzezhuang/p/3735998.html