I have some questions about KMeans and clustering. I'm generating
matrices from recommendation data models. To decide whether the
generated matrices have interesting data, I'm generating and charting
KMeans clusters. Next, I'm mapping all of the vectors in the matrix to a
nearest "corner" and then clustering those corners.
The idea of the corners is to create a nearest-neighbors database in
advance of searching, to allow very quick lookup of close-by neighbors.
I can't tell if the results make sense. Please have a look.
http://ultrawhizbang.blogspot.com/2010/11/kmeans-cluster-testing-part-second.html
http://ultrawhizbang.blogspot.com/2010/11/kmeans-cluster-testing-part-second.html
Lance Norskog wrote:
It's not possible to do text+chart conversations here, so I broke down
and started a technical blog.
The topic is using using Canopy to supply KMeans. With outputs for
training data, test data and random data.
http://ultrawhizbang.blogspot.com/2010/11/kmeans-cluster-testing.html
