KMeans clustering for more than 5 million vectors

Question

I have hit a real problem. I need to do some Kmeans clustering for 5 million vectors, each containing about 32 cols. I tried out Mahout which requires linux and I am on windows, I am restrained from using a Linux OS and any sort of simulator.

Can anyone suggest a KMeans clustering algorithm that is scalable upto 5M vectors and can converge quickly?

I have tested a few but they wont scale. Which means they are slow and take forever to complete.

Thanks

Answer 1

OK, So who ever wants clustering for large scale datasets, the only way of doing so is to use Mahout. IT requires a linux platform. So I had to use virtual box, placed Ubuntu on it and then used Mahout. Its a lengthy procedure to set up Mahout, but the two links that I used are as follows.

http://www.michael-noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_(Single-Node_Cluster)

http://www.michael-noll.com/wiki/Running_Hadoop_On_Ubuntu_Linux_(Multi-Node_Cluster)