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The Muster library provides implementations of sequential and parallel K-Medoids clustering algorithms. It is intended as a general framework for parallel cluster analysis, particularly for performance data analysis on systems with very large numbers of processes.
The parallel implementations in the Muster are designed to perform well even in environments where the data to be clustered is entirely distributed. For example, many performance tools need to analyze one data element from each process in a system. To analyze this data efficiently, clustering algorithms that move as little data as possible are required. In Muster, we exploit sampled clustering algorithms to realize this efficiency.
The parallel algorithms in Muster are implemented using the Message Passing Interface (MPI), making them suitable for use on many of the world’s largest supercomputers. They should, however, also run efficiently on your laptop.
More information on Muster (how to use it, etc.) can be found in the official documentation.
Installation instructions for Muster can be found in the INSTALL file in the distribution tarball.
The http://tgamblin.github.com/muster/classcluster_1_1par__kmedoids.html parallel clustering algorithm implemented in Muster is described in this paper from ICS 2010:
- Todd Gamblin, Bronis R. de Supinski, Martin Schulz, Robert J. Fowler, and Daniel A. Reed. Clustering performance data efficiently at massive scales. In International Conference on Supercomputing (ICS’10), Tsukuba, Japan, June 1-4 2010.