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
- Snippet from Wikipedia: Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).
A good tutorial of clustering algorithms is here.
The algorithms can 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.
You can run K-means and hierarchical clustering algorithms in the following modes:
- K-means algorithm fixed cluster mode with single seed event
- K-means algorithm for multiple iterations
- K-means clustering using exchange method for best estimate
- K-means clustering using exchange method
- Hierarchical clustering algorithm
- Hierarchical clustering algorithm, best estimate
Download and install
Download jMinHep from the JMinHep 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. You can also run jMinHep inside ScaVis. There is a dedicated example using Java scripting in the ScaVis manual.
After downloading the package from the JMinHep web page, unzip the file and run it. Here is an example for Mac/Linux:
unzip jminhep-2.0.zip cd jminhep java -jar jminhep.jar
For Windows, unzip the file and launch it by clicking on the jar file “jminhep.jar”.
You will see the GUI:
How to run and examples
— S.Chekanov 2013/11/29 21:54