# Visualizing high dimensional binary data

What is a good way to visualize high dimensional (say n=10) binary data? I remember reading something about that a few years ago.

Say for instance, you want to plot / cluster pizzas based on their topping, e.g. ham, chicken, mushrooms etc.

-

Even if this is binary, you can do a scaled Principal Component Analysis (PCA). By projecting the results on the 2D plane of the first Principal Components you get an idea of the clustering of your data.

In R:

# data is your data.frame/matrix of data
pca <- prcomp(data, scale.=TRUE)
# Screeplot to see how much variance is in the 2D plane
plot(pca)
# Projections
plot(data %*% pca\$rotation[,1:2])

-
High dimensions makes visualization difficult for any variables (continuous or binary). But projections into best separating 2D space should be helpful which is what gui11aume suggests. –  Michael Chernick Jun 7 '12 at 11:20