R语言之动态聚类分析_r语言聚类分析-数据分析

R语言之动态聚类分析

关键词：r语言聚类分析、r语言 kmeans聚类分析、r语言聚类分析实例、r语言聚类分析包、r语言做聚类分析、r语言做变量聚类分析

1、关键点

系统聚类分析与动态聚类分析的比较：

a.系统聚类分析
#系统聚类一次形成之后就不能再改变这需要一次分得比较准确对分类的方法有很高的要求

#相应的计算量也会很大比如Q型系统聚类法聚类的过程是在样本间距离矩阵的基础上进行的

#当样本容量很大时需要占用足够的计算机内存而且在并类过程中需要将每类样本和其他样本间的距离
#逐一加一比较以决定应该合并的类别需哟很长的计算时间

b.动态聚类分析

#动态聚类法又称为逐步聚类法基本思想是开始先粗略地分一下类然后按照某种最优原则修改不合理的分类
#直至类分得比较合理为止 适用于大样本的Q型聚类分析

2、函数

#R语言中提供的动态聚类分析的函数 kmeans()函数
#kmeans()函数采用的是K-均值方法是采用逐个修改的方法
#kmeans()函数形式 kmeans(x,centers,iter.max=10,nstart=1,algorithm=c())
#x是由数据构成的矩阵或者数据框 centers是聚类的个数或者是初始类的中心
#iter.max是最大迭代次数缺省时为10
#nstart随机集合的个数(当centers为聚类的个数时)

#algorithm为动态聚类的算法 “Hartigan-Wong”（缺省时）, “Lloyd”,”Forgy”, “MacQueen

3、案例分析
#1.载入原始数据并作相关处理

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”)