从您的示例中很难分辨出哪个变量定义了您的集群。下面是一个使用ggplot2
和data.table
(改编自此处)的一些模拟集群的示例。
library(data.table)
library(ggplot2)
# Simulate data:
set.seed(1)
n_cluster = 50
centroids = cbind.data.frame(
x=rnorm(5, mean = 0, sd=5),
y=rnorm(5, mean = 0, sd=5)
)
dt = rbindlist(
lapply(
1:nrow(centroids),
function(i) {
cluster_dt = data.table(
x = rnorm(n_cluster, mean = centroids$x[i]),
y = rnorm(n_cluster, mean = centroids$y[i]),
cluster = i
)
}
)
)
dt[,cluster:=as.factor(cluster)]
# Find convex hull of each point by cluster:
hulls = dt[,.SD[chull(x,y)],by=.(cluster)]
# Plot:
p = ggplot(data = dt, aes(x=x, y=y, colour=cluster)) +
geom_point() +
geom_polygon(data = hulls,aes(fill=cluster,alpha = 0.5)) +
guides(alpha=F)
这会产生以下输出:

编辑
如果您没有预定义的集群,则可以使用集群算法。作为一个简单的示例,请参见下面的使用kmeans
5 个质心的解决方案。
# Estimate clusters (e.g. kmeans):
dt[,km_cluster := as.factor(kmeans(.SD,5)$cluster),.SDcols=c("x","y")]
# Find convex hull of each point:
hulls = dt[,.SD[chull(x,y)],by=.(km_cluster)]
# Plot:
p = ggplot(data = dt, aes(x=x, y=y, colour=km_cluster)) +
geom_point() +
geom_polygon(data = hulls,aes(fill=km_cluster,alpha = 0.5)) +
guides(alpha=F)
在这种情况下,估计集群的输出几乎等同于构建的集群。
