You haven't written anything about your interest. I know algorithms in graph mining has been implemented over the Hadoop framework. This software http://www.cs.cmu.edu/~pegasus/ and paper: "PEGASUS: A Peta-Scale Graph Mining System - Implementation and Observations" may give you a starting point.
Further, this link discusses something similar to your question: http://atbrox.com/2010/02/08/parallel-machine-learning-for-hadoopmapreduce-a-python-example/ but it is in python. And, there is a very good paper by Andrew Ng "Map-Reduce for Machine Learning on Multicore".
There was a NIPS 2009 workshop on the similar topic "Large-Scale Machine Learning: Parallelism and Massive Datasets". You can browse some of the paper and get an idea.
Edit : Also there is Apache Mahout http://mahout.apache.org/ -->" Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm"