我正在data.frame使用分类变量和度量变量
# example data
a <- as.factor(c("A","A","B","C","D","A","C","A","C","C"))
b <- rep(1:5,2)
c <- as.factor(c("elephant","elephant","cat","dog","cat","elephant",
"cat","elephant","dog","dog"))
df <- data.frame(a,b,c)
我对此示例数据进行了聚类分析
# Dissimilarity Matrix Calculation
library(cluster)
x <- daisy(df, metric = c("gower"),
stand = FALSE, type = list())
# Hierarchical Clustering
z <- agnes(x, diss = inherits(x, "dist"), metric = "euclidean",
stand = FALSE, method = "single", par.method,
trace.lev = 0, keep.diss = TRUE)
并收到这个树状图
plot(z, main="plotit", which.plot = 2)
- 我怎么知道在哪里砍树？
我可以做类似的事情
cutree(z, k = 2, h=0.3)
但是为k和选择的值h完全是任意的。我在一个大型数据集上工作，我不能依赖我在这个例子的图中看到的信息？
- 是否有确定聚类数量的启发式方法？
- 是否有启发式方法来确定树的切割高度？
