ML_R Kmeans

Kmeans作为机器学习中入门级算法，涉及到计算距离算法的选择，聚类中心个数的选择。下面就简单介绍一下在R语言中是怎么解决这两个问题的。

参考Unsupervised Learning with R

> Iris<-iris

> #K mean

> set.seed(123)

> KM.Iris<-kmeans(Iris[1:4],3,iter.max=1000,algorithm = c("Forgy"))

> KM.Iris$size

[1] 50 39 61

> KM.Iris$centers  #聚类的3个中心

  Sepal.Length Sepal.Width Petal.Length Petal.Width

1     5.006000    3.428000     1.462000    0.246000

2     6.853846    3.076923     5.715385    2.053846

3     5.883607    2.740984     4.388525    1.434426

> str(KM.Iris)

List of 9

 $ cluster     : int [1:150] 1 1 1 1 1 1 1 1 1 1 ...

 $ centers     : num [1:3, 1:4] 5.01 6.85 5.88 3.43 3.08 ...

  ..- attr(*, "dimnames")=List of 2

  .. ..$ : chr [1:3] "1" "2" "3"

  .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"

 $ totss       : num 681

 $ withinss    : num [1:3] 15.2 25.4 38.3

 $ tot.withinss: num 78.9

 $ betweenss   : num 603

 $ size        : int [1:3] 50 39 61

 $ iter        : int 2

 $ ifault      : NULL

 - attr(*, "class")= chr "kmeans"

> table(Iris$Species,KM.Iris$cluster)

              1  2  3

  setosa     50  0  0

  versicolor  0  3 47

  virginica   0 36 14

> Iris.dist<-dist(Iris[1:4])

> Iris.mds<-cmdscale(Iris.dist)

关于cmdscale,classical multidimensional scaling of a data matrix,也被成为是principal coordinates analysis

> par(mfrow=c(1,2))

> #3D

> library("scatterplot3d")

> chars<-c("1","2","3")[as.integer(iris$Species)]

> g3d<-scatterplot3d(Iris.mds,pch=chars)

> g3d$points3d(iris.mds,col=KM.Iris$cluster,pch=chars)

> #2D

> plot(Iris.mds,col=KM.Iris$cluster,pch=chars,xlab="Index",ylab= "Y")

> KM.Iris[1]

$cluster

  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 2 3 3 3 3 3

 [59] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 2 2 2 3 2 2 2 2 2 2 3 3 2

[117] 2 2 2 3 2 3 2 3 2 2 3 3 2 2 2 2 2 3 2 2 2 2 3 2 2 2 3 2 2 2 3 2 2 3

> Iris.cluster<-cbind(Iris,KM.Iris$cluster)

> head(Iris.cluster)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species KM.Iris$cluster

1          5.1         3.5          1.4         0.2  setosa               1

2          4.9         3.0          1.4         0.2  setosa               1

3          4.7         3.2          1.3         0.2  setosa               1

4          4.6         3.1          1.5         0.2  setosa               1

5          5.0         3.6          1.4         0.2  setosa               1

6          5.4         3.9          1.7         0.4  setosa               1

> # 下面寻找最佳簇数目

> # 30 Kmeans loop

> InerIC<-rep(0,30);InerIC

 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

> for (k in 1:30){

+ set.seed(123)

+ groups=kmeans(Iris[1:4],k)

+ InerIC[k]<-groups$tot.withinss

+ }

> InerIC

 [1] 681.37060 152.34795  78.85144  57.26562  46.46117  39.05498  37.34900  32.58266  28.46897  26.32133  24.92591

[12]  23.52298  23.33464  21.83167  20.04231  19.21720  17.82750  17.35801  16.69589  15.74660  14.53898  13.61800

[23]  13.38004  12.81350  12.37310  12.02532  11.72245  11.55765  11.04824  10.56507

> groups

K-means clustering with 30 clusters of sizes 5, 4, 1, 5, 7, 5, 9, 3, 4, 2, 3, 4, 3, 7, 4, 5, 8, 5, 4, 3, 9, 1, 6, 4, 4, 8, 1, 3, 10, 13

Cluster means:

   Sepal.Length Sepal.Width Petal.Length Petal.Width

1      4.940000    3.400000     1.680000   0.3800000

2      7.675000    2.850000     6.575000   2.1750000

3      5.000000    2.000000     3.500000   1.0000000

4      7.240000    2.980000     6.020000   1.8400000

5      6.442857    2.828571     5.557143   1.9142857

6      4.580000    3.320000     1.280000   0.2200000

7      6.722222    3.000000     4.677778   1.4555556

8      6.233333    3.300000     4.566667   1.5666667

9      6.150000    2.900000     4.200000   1.3500000

10     5.400000    2.800000     3.750000   1.3500000

11     6.133333    2.700000     5.266667   1.5000000

12     6.075000    2.900000     4.625000   1.3750000

13     5.000000    2.400000     3.200000   1.0333333

14     6.671429    3.085714     5.257143   2.1571429

15     4.400000    2.800000     1.275000   0.2000000

16     5.740000    2.700000     5.040000   2.0400000

17     5.212500    3.812500     1.587500   0.2750000

18     5.620000    4.060000     1.420000   0.3000000

19     5.975000    3.050000     4.900000   1.8000000

20     7.600000    3.733333     6.400000   2.2333333

21     6.566667    3.244444     5.711111   2.3333333

22     4.900000    2.500000     4.500000   1.7000000

23     5.550000    2.450000     3.816667   1.1333333

24     6.275000    2.625000     4.900000   1.7500000

25     5.775000    2.700000     4.025000   1.1750000

26     5.600000    2.875000     4.325000   1.3250000

27     6.000000    2.200000     4.000000   1.0000000

28     6.166667    2.233333     4.633333   1.4333333

29     4.840000    3.080000     1.470000   0.1900000

30     5.146154    3.461538     1.438462   0.2230769

Clustering vector:

  [1] 30 29  6 29 30 17  6 30 15 29 17  1 29 15 18 18 18 30 18 17 30 17  6  1  1 29  1 30 30 29 29 30 17 18 29 29 30 30 15

 [40] 30 30 15  6  1 17 29 17  6 17 30  7  8  7 23  7 26  8 13  7 10  3  9 27 12 10  7 26 25 28 23 19  9 24 12  9  7  7  7

 [79] 12 23 23 23 25 11 26  8  7 28 26 23 26 12 25 13 26 26 26  9 13 25 21 16  4  5 21  2 22  4  5 20 14  5 14 16 16 14  5

[118] 20  2 28 21 16  2 24 21  4 24 19  5  4  4 20  5 11 11  2 21  5 19 14 21 14 16 21 21 14 24 14 21 19

Within cluster sum of squares by cluster:

 [1] 0.2880000 0.5325000 0.0000000 0.4200000 0.6371429 0.2920000 0.7933333 0.1400000 0.2400000 0.1500000 0.2533333

[12] 0.1025000 0.1066667 0.5571429 0.4275000 0.2960000 0.5012500 0.5280000 0.1175000 0.5933333 1.0311111 0.0000000

[23] 0.3716667 0.1850000 0.1025000 0.5450000 0.0000000 0.2866667 0.3700000 0.6969231

 (between_SS / total_SS =  98.4 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss" "betweenss"    "size"

[8] "iter"         "ifault"      

> plot(InerIC,col = "black",lty =3)

There were 18 warnings (use warnings() to see them)

> abline(v=4,col="black",lty=3)

> text (4,60,"4 clusters",col="black",adj = c(0,-0.1),cex=0.7)

> library(NbClust)

> data<-Iris[,-5]

> head(data)

  Sepal.Length Sepal.Width Petal.Length Petal.Width

1          5.1         3.5          1.4         0.2

2          4.9         3.0          1.4         0.2

3          4.7         3.2          1.3         0.2

4          4.6         3.1          1.5         0.2

5          5.0         3.6          1.4         0.2

6          5.4         3.9          1.7         0.4

> best<-NbClust(data,diss=NULL,distance ="euclidean",min.nc=2, max.nc=15, method = "complete",index = "alllong")

*** : The Hubert index is a graphical method of determining the number of clusters.

                In the plot of Hubert index, we seek a significant knee that corresponds to a

                significant increase of the value of the measure i.e the significant peak in Hubert

                index second differences plot.

*** : The D index is a graphical method of determining the number of clusters.

                In the plot of D index, we seek a significant knee (the significant peak in Dindex

                second differences plot) that corresponds to a significant increase of the value of

                the measure. 

*******************************************************************

* Among all indices:

* 2 proposed 2 as the best number of clusters

* 15 proposed 3 as the best number of clusters

* 5 proposed 4 as the best number of clusters

* 1 proposed 6 as the best number of clusters

* 1 proposed 14 as the best number of clusters

* 3 proposed 15 as the best number of clusters 

                   ***** Conclusion *****                            

* According to the majority rule, the best number of clusters is  3 

*******************************************************************

> table(names(best$Best.nc[1,]),best$Best.nc[1,])

             0 1 2 3 4 6 14 15

  Ball       0 0 0 1 0 0  0  0

  Beale      0 0 0 1 0 0  0  0

  CCC        0 0 0 1 0 0  0  0

  CH         0 0 0 0 1 0  0  0

  Cindex     0 0 0 1 0 0  0  0

  DB         0 0 0 1 0 0  0  0

  Dindex     1 0 0 0 0 0  0  0

  Duda       0 0 0 0 1 0  0  0

  Dunn       0 0 0 0 0 0  0  1

  Frey       0 1 0 0 0 0  0  0

  Friedman   0 0 0 0 1 0  0  0

  Gamma      0 0 0 0 0 0  1  0

  Gap        0 0 0 1 0 0  0  0

  Gplus      0 0 0 0 0 0  0  1

  Hartigan   0 0 0 1 0 0  0  0

  Hubert     1 0 0 0 0 0  0  0

  KL         0 0 0 0 1 0  0  0

  Marriot    0 0 0 1 0 0  0  0

  McClain    0 0 1 0 0 0  0  0

  PseudoT2   0 0 0 0 1 0  0  0

  PtBiserial 0 0 0 1 0 0  0  0

  Ratkowsky  0 0 0 1 0 0  0  0

  Rubin      0 0 0 0 0 1  0  0

  Scott      0 0 0 1 0 0  0  0

  SDbw       0 0 0 0 0 0  0  1

  SDindex    0 0 0 1 0 0  0  0

  Silhouette 0 0 1 0 0 0  0  0

  Tau        0 0 0 1 0 0  0  0

  TraceW     0 0 0 1 0 0  0  0

  TrCovW     0 0 0 1 0 0  0  0

> hist(best$Best.nc[1,],breaks = max(na.omit(best$Best.nc[1,])))

> barplot(table(best$Best.nc[1,]))

> # 选择最佳聚类算法algorithm

> Hartigan <-0

> Lloyd <- 0

> Forgy <- 0

> MacQueen <- 0

> set.seed(123)

> # 做500次Kmeans计算，3个聚类中心，每次计算，每种算法迭代最多1000次

> for (i in 1:500){

+ KM<-kmeans(Iris[1:4],3,iter.max = 1000,algorithm = "Hartigan-Wong")

+ Hartigan <- Hartigan + KM$betweenss

+ KM<-kmeans(Iris[1:4],3,iter.max = 1000,algorithm = "Lloyd")

+ Lloyd <- Lloyd + KM$betweenss

+ KM<-kmeans(Iris[1:4],3,iter.max = 1000,algorithm = "Forgy")

+ Forgy <- Forgy + KM$betweenss

+ KM<-kmeans(Iris[1:4],3,iter.max = 1000,algorithm = "MacQueen")

+ MacQueen <- MacQueen + KM$betweenss

+ }

> # 输出结果

> Methods <- c("Hartigan","Lloyd","Forgy","MacQueen")

> Results <- as.data.frame(round(c(Hartigan,Lloyd,Forgy,MacQueen)/500,2))

> Results <- cbind(Methods,Results)

> names(Results) <- c("Method","Betweenss")

> Results

    Method Betweenss

1 Hartigan    590.76

2    Lloyd    589.38

3    Forgy    590.63

4 MacQueen    590.05

> #作图

> par(mfrow =c(1,1))

> KM<-kmeans(Iris[1:4],3,iter.max = 1000,algorithm = "Hartigan-Wong")

> library(cluster)

> clusplot(Iris[1:4],KM$cluster,color = T,shade = T,lables=2,lines = 1,main = "Iris聚类分析")

There were 50 or more warnings (use warnings() to see the first 50)

> library(HSAUR)

There were 50 or more warnings (use warnings() to see the first 50)

> diss <- daisy(Iris[1:4])

> dE2 <- diss^2

> obj <-silhouette(KM$cluster,dE2)

> plot(obj,col=c("red","green","blue"))

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