n = 100g = 6 set.seed(g)d <- data.frame(x = unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))), y = unlist(lapply(1:g, function(i) rnorm(n/g, runif(1)*i^2))))plot(d)###################d = read.table('clipboard',header = T)plot(d)mydata <- dwss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))for (i in 2:15) wss[i] <- sum(kmeans(mydata, centers=i)$withinss)plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares")library(fpc)pamk.best <- pamk(d)##############################lastcluster = pam(d, 1) # pam(d, pamk.best$nc)plot(d,type='l')vl = c(lastcluster$medoids[,1])vlabline(v=vl,lty=2,col='red')#library(cluster)#plot(pam(d, 3))####################################cat("number of clusters estimated by # optimum average silhouette width:", pamk.best$nc, "\n")#library(cluster)#plot(pam(d, pamk.best$nc))
转载于:https://www.cnblogs.com/arcserver/p/9186004.html
