a framework for data clustering


jMinHEP is a framework for clustering analysis, i.e. for non-supervised learning in which the classification process does not depend on a priory information. The program is a pure JAVA-based application and includes the following algorithms:
  • K-means clustering analysis (single and multi pass)
  • C-means (fuzzy) algorithm
  • Agglomerative hierarchical clustering
The algorithms run for a fixed cluster mode and for a best estimate, i.e. when the number of clusters is not a priory given but is found after estimation of the cluster compactness. The data points can be defined in multidimensional space. At present, the distance measure is euclidean.

The program can run in the GUI mode where all clustering parameters can be set via the user interface and the output is displayed. The data can be loaded in form of Attribute-Relation File Format (ARFF). The cluster centers and the seeds positions can also be shown.

Posted by S.Chekanov ( on Jun 01, 2012
Tags: About jMinHep

Download and run

Download the jMinHep from the download web page. This version is completely self-contained and does not require additional libraries. The source code is included. The program is a part of the SCaVis computational environment.

After download, unzip the file and run it. Here is an example for Mac/Linux:

cd jminhep
java -jar jminhep.jar
For Windows, unzip the file and launch it by clicking on the jar file "jminhep.jar".


Reference manual and Java examples that use jMinHep library are available for SCaVis members using the jMinHep manual wiki.

License and source code

The project is licensed by the GNU public license.