I'm a great believer in looking at the data. That is, checking it out visually. This is mainly as a sanity check. How real are these clusters? Did I forget to scale the data so that everything is clustered on the basis of the variable with the biggest range?
The following R code clusters using the non-hierarchical method k-means clustering (so no nice dendrogram). Once all the points have been assigned to a particular cluster you can look at the data in 2D or 3D (using principal coordinate analysis, aka classical multidimensional scaling) and colour the points by cluster:
Thanks to Rajarshi for pointing out how to generate the interactive 3D plot.
data <- read.table("Boston.txt")
data <- scale(data)
myclust <- kmeans(data, 10)
# Represent the data in 2D and colour by cluster
distances <- dist(data, method="euclidean")
mycmdscale <- cmdscale(distances, 2)
# Let's try 3D (you need to install scatterplot3d first)
mycmdscale <- cmdscale(distances, 3)
s3d <- scatterplot3d(mycmdscale, color=myclust$cluster)
# Let's try interactive 3D
library(rgl) # Need to install this package first
plot3d(mycmdscale, col=myclust$cluster, size=5)