I know dendrograms are quite popular. However if there are quite large number of observations and classes it hard to follow. However sometime I feel that there should be better way to present the same thing. I got an idea but do not know how to implement it.
Consider the following dendrogram.
> data(mtcars)
> plot(hclust(dist(mtcars)))
Can plot it like a scatter plot. In which the distance between two points is plotted with line, while sperate clusters (assumed threshold) are colored and circle size is determined by value of some variable.
You are describing a fairly typical way of going about cluster analysis:
- Use a clustering algorithm (in this case hierarchical clustering)
- Decide on the number of clusters
- Project the data in a two-dimensional plane using some form or principal component analysis
The code:
hc <- hclust(dist(mtcars))
cluster <- cutree(hc, k=3)
xy <- data.frame(cmdscale(dist(mtcars)), factor(cluster))
names(xy) <- c("x", "y", "cluster")
xy$model <- rownames(xy)
library(ggplot2)
ggplot(xy, aes(x, y)) + geom_point(aes(colour=cluster), size=3)
What happens next is that you get a skilled statistician to help explain what the x and y axes mean. This usually involves projecting the data to the axes and extracting the factor loadings.
The plot:
来源：https://stackoverflow.com/questions/11462901/cluster-presentation-dendrogram-alternative-in-r