R语言系统聚类分析1

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#聚类分析是一类将数据所研究对象进行分类的统计方法，这一类方法的共同特点是：
#事先不知道类别的个数与结构据以进行分类的数据是对象之间的相似性或差异性数据
#将这些相似（相异）性数据看成是对象之间的距离远近的一种度量将距离近的对象
#归入一类不同类之间的对象距离较远
#聚类分析根据分类对象不同分为Q型聚类分析(指的是对样本进行聚类) 和R型聚类分析(指的是对变量进行聚类)

#距离和相似系数
#聚类分析是研究对样本或变量的聚类变量可以分为两类1定量变量通常指的是连续量
#2 定性变量(有序变量+名义变量) 这些量并非真有数量上的变化而只有性质上的差异

#1.距离 1绝对值距离棋盘距离城市街区距离 2 Euclide1距离 3Minkowski1距离 4切比雪夫距离 5 Mahalanobis距离 6LanceWilliams距离
#2.数据中心化标准化变换中心化变换标准化变换极差标准化变换极差正规化变换
#3.相似系数

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","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") )
d=dist(scale(X)) #scale对数据做中心化或者标准化处理
hc1<-hclust(d) #hclust提供系统聚类的计算最长距离法
plclust(hc1, hang=-) #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
re1<-rect.hclust(hc1, k=, border="red")