Clustering Worksheet

• Web-based gene expression data clustering
  The clustering algorithms are implemented in the Pycluster, an extension module for Python that implements common clustering algorithms such a k-means and hierarchical clustering. Clustering on the Web is integrated with Java TreeView, which is used to visualize the clustering result.

• CClusteringLibrary.pdf

Open Source Clustering Software

• Home Page
  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 Labs. 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.

• Pycluster page
  Python is a scripting language similar to Perl. It has excellent support for numerical work through the Numerical Python package, providing a functionality similar to Matlab and S. This makes Python together with Numerical Python an ideal tool for analyzing cDNA expression data. Numerical Python is under active development at Lawrence Livermore National Laboratory.

• Python at Livermore Computing (LC)
  This web page provides a one-stop overview of efforts and resources available to you for Python at Livermore Computing (LC). Here, you can find information about Python, associated packages, new techniques, and personnel contacts. If you want to delve deeper into Python-related issues, there is also a list of resources.