为什么 kmeans 找不到 3 个集群？

Question

我运行一个 3 维数据集上的 kmeans 并得到以下结果：

代码如下：

library(tidyr)

setwd('C:/temp/rwd')
getwd()

df <- read.table('data-1581352459203.csv', 
                 header = TRUE,
                 sep = ",")

dff <- df %>% pivot_wider(names_from = SensorId, values_from = last)

data = data.frame(dff$`3`, dff$`4`, dff$`5`)
cf.kmeans <- kmeans(data, centers = 3, nstart = 20)
cf.kmeans

library(plot3D)
x <- dff$`3`
y <- dff$`4`
z <- dff$`5`
scatter3D(x, y, z, 
          bty ="g", pch = cf.kmeans$cluster, colvar=as.numeric(cf.kmeans$cluster),
          xlab = "Temperature", ylab = "Humidity", zlab = "Speed",
          ticktype = "detailed")

library("plot3Drgl")
plotrgl()

数据集看起来像这样（90 个观察）：

如果能解释为什么 kmeans 找不到明显的聚类，我将不胜感激。

Answer 1

您的变量在不同的范围内。您需要缩放数据，否则更大规模的变量将占主导地位。请参阅下面的可重现示例：

library(plot3D)
set.seed(100)
mat = cbind(rnorm(60,rep(c(0,30,30),each=20),5),
            rnorm(60,rep(c(0,30,30),each=20),5),
            rnorm(60,rep(c(0,0,1),each=20),0.1)
)
clus = kmeans(mat,3,nstart = 20)

scatter3D(mat[,1],mat[,2],mat[,3],
          ticktype = "detailed",colvar=clus$cluster)

以上与您的结果相似，现在做缩放：

clus=kmeans(scale(mat),3,nstart=20)
scatter3D(mat[,1],mat[,2],mat[,3],ticktype = "detailed",colvar=clus$cluster)

为什么 kmeans 找不到 3 个集群？

Why doesn't kmeans find the 3 clusters?

r

multidimensional-array

k-means

plot3d