Parallel clustering on a commodity supercomputer

k-means based clustering algorithms have interesting performances in several application fields. The computational complexity of these techniques depends on the size of the data set and the codebook. The larger the data set and the codebook, the greater the computing time to reach the convergence. This paper illustrates the behaviour of two clustering algorithms we have realized and parallelized on a commodity supercomputer.