Distributed unsupervised learning using the multisoft machine

Unsupervised learning using K-means techniques is successfully employed in several application fields. When the training set and the number of reference vectors increases, the computational effort can become prohibitive for mono-processor Computers. This paper illustrates the parallelization of two clustering techniques using the MULTISOFT machine, a commodity supercomputer, built at the University of Messina. The particular management policy of the MULTISOFT machine and the implementation techniques have shown very interesting results: the speedup increases together with the complexity of the problem to be solved. (C) 2002 Published by Elsevier Science Inc.