|32 bit||64 bit||(32/64 bit only for executables)|
|lnxcls.zip||(594 kb)||lnxcls.zip||(902 kb)||GNU/Linux executables|
|wincls.zip||(520 kb)||wincls.zip||(554 kb)||Windows console executables|
|cluster.zip||(272 kb)||cluster.tar.gz||(236 kb)||C sources, package version 4.2 (2016.02.25)|
Note: The table package contains some auxiliary programs for preprocessing the data files.
Cluster is a set of programs to do probabilistic clustering (expectation maximization algorithm to find a mixture of Gaussians) and fuzzy clustering (fuzzy c-means algorithm, Gustafson-Kessel algorithm, and Gath-Geva / FMLE algorithm) and to execute the induced set of clusters on new data. The programs are highly parameterizable, so that a large variety of clustering approaches can be carried out. Since version 2.4 the program also contains a competitive learning / learning vector quantization mode, which will eventually supersede the learning vector quantization programs. A brief description of how to apply these programs can be found in the file cluster/ex/readme in the source package.
If you have trouble executing the programs on Microsoft Windows, check whether you have the Microsoft Visual C++ Redistributable for Visual Studio 2017 (see under "Other Tools and Frameworks") installed, as the programs were compiled with Microsoft Visual Studio 2017.
An extensive treatment of the clustering approaches supported by the programs can be found in my habilitation thesis:
A description of the ideas underlying the neural network based update methods can also be found in:
A description of the regularization methods can also be found in:
A description of learning vector quantization with size and shape parameters can be found in: