The open source clustering software available here contains clustering routines that can be used to analyze gene expression data. Routines for hierarchical (pairwise simple, complete, average, and centroid linkage) clustering, k-means and k-medians clustering, and 2D self-organizing maps are included. The routines are available in the form of a C clustering library, an extension module to Python, a module to Perl, as well as an enhanced version of Cluster, which was originally developed by Michael Eisen of Berkeley Lab. The C clustering library and the associated extension module for Python was released under the Python license. The Perl module was released under the Artistic License. Cluster 3.0 is covered by the original Cluster/TreeView license.

Cluster 3.0 for Windows, Mac OS X, Linux, Unix


Algorithm::Cluster for Perl

Reference: M. J. L. de Hoon, S. Imoto, J. Nolan, and S. Miyano: Open Source Clustering Software. Bioinformatics, 20 (9): 1453--1454 (2004).

Laboratory of DNA Information Analysis
Human Genome Center
Institute of Medical Science
University of Tokyo

© 2002, Michiel de Hoon, All rights reserved.