我在研究中使用了k-means和sk-means。在K-means聚类中,为了获得聚类,
# k-means clustering of tweets
k<-6
kmeansResult<-kmeans(m3,k)
# Cluster centers
round(kmeansResult$centers,digits=3)
for(i in 1:k){
at(paste("cluster",i,":",sep=""))
s<-sort(kmeansResult$centers[i,],decreasing=T)
cat(names(s)[1:3],"\n")
}
对于sk-means聚类,我设法直到
m3_new <- m3[slam::row_sums(m3) > 0, ]
skmeansResult<-skmeans(m3_new,k)
我应该采取哪些步骤来获得群集结果?
答案 0 :(得分:0)
km_clus <- kmeansResult$cluster
sk_clus <- skmeansResult$cluster