How do I interpret my result of clustering?
K-means don't modify the underlying structure of your data. K-means will just provide the 'color' part of your graph. 

To answer the question about why do you get a cuboid, it's because your underlying data are a cuboid. Not necessarily by construction, but that's what happen when you cap your data. As an exemple, look at the following code :

X1 = c(rnorm(1000))
X2 = c(rnorm(1000))
q95_1 = quantile(X1,0.95)
q95_2 = quantile(X2,0.95)
q5_1 = quantile(X1,0.05)
q5_2 = quantile(X2,0.05)
X1[X1&gt;q95_1]=q95_1
X2[X2&gt;q95_2]=q95_2
X1[X1&lt;q5_1]=q5_1
X2[X2&lt;q5_2]=q5_2
plot(X1,X2)


The code simulate two random gaussian and cap them at 5% and 95%.

this is what you get :



Notice the squaroid pattern ? This is why you get a cuboid in 3D. 

Ps: I can't help but say that's what you get when you do k-means without properly looking at your variables (see: What value can I gain by doing exploratory data analysis on features (and thus data) before doing clustering? for an infinite loop).