• CompClustTk v0.2.12 Released
  • CompClust v0.2 Released


CompClust is a python package written using the pyMLX and IPlot APIs. It provides software tools to explore and quantify relationships between clustering results. Its development has been largely built around needs of microarray data analysis but could be easily used in other domains.

Briefly pyMLX provides an provides for efficient and convenient execution of many clustering algorithms using a extendable library of algorithms. It also provides many-to-many linkages between data features and annotations (such as cluster labels, gene names, gene ontology information, etc.) This linkages are are persistant through data manipulations. IPlot provides an abstraction of the plotting process in which any arbitrary feature or derived feature of the data can be projected onto any feature of the plot, including the X,Y coordinates of points, marker symbol, marker size, maker/line color, etc. These plots are intrinsically linked to the dataset, the View and the Labeling classes found within pyMLX.


Windows CompClustTk Standalone Installer

For your convinance, we have packaged up everything you need to use CompClustTk in one simple to use install package.

Program License Release Date Download
CompClustTk v0.2.14 w/ Docs v0.1.10 MLX PUBLIC LICENSE V1.0 2004May12 Download

Be aware that this installer includes the following modules/libraries, which you can install independantly if you desire:

Python2.3PythonSee LinkLink
IPythonLGPLSee LinkLink
NumPyOSI ApprovedNumeric-23.1.win32-py2.3.exe

Windows CompClust Python Package Installation

Run the following installation packages in the order provided... They can be downloaded from the links listed in the table above.

Download Source

Python source package of compClust is available below (includes pyMLX and IPlot).


The Python language's website. Plenty of good resources regarding python
IPython provides an enhanced interactive shell with tab-completion, object introspection, online help, history and many other nice features. I highly recommend using IPython, here are compClust specfic IPython configuration files: Numeric/Scientfic python and compClust. Save these files into your IPYTHONDIR and then start ipython with the compClust profile by using the -p option: ipython -p compClust