I want to run k-means of MLib on a big dataset, it seems for big datsets, we
need to perform pre-clustering methods such as canopy clustering. By starting
with an initial clustering the number of more expensive distance measurements
can be significantly reduced by ignoring points outside of the initial
canopies.
I I am not mistaken, in the k-means of MLib, there are three initialization
steps : Kmeans ++, Kmeans|| and random .
So, can anyone explain to me that can we use kmeans|| instead of canopy
clustering? or these two methods act completely different?
Best Regards
.......................................................
Amin Mohebbi
PhD candidate in Software Engineering
at university of Malaysia
Tel : +60 18 2040 017
E-Mail : tp025...@ex.apiit.edu.my
amin_...@me.com
