VIKAMINE: Subgroup Discovery and Analytics

VIKAMINE - An extensible open-source rich-client environment and platform for subgroup discovery and analytics.

Software Releases/Code: VIKAMINE@Sourceforge (License: GNU LGPL)

The R subgroup package (rsubgroup) provides a wrapper around the VIKAMINE core.

About VIKAMINE

VIKAMINE is an integrated rich-client environment for subgroup discovery and analytics. The acronym VIKAMINE stands for

Visual,
Interactive,
Knowledge-Intensive,
Analytics, and
MINing Environment.

VIKAMINE features several powerful and intuitive visualizations complemented by fast automatic mining methods.

VIKAMINE is an open-source project hosted at Sourceforge, directed by Martin Atzmueller and Florian Lemmerich.
The VIKAMINE software is released under the GNU Lesser General Public License (LGPL), see the GNU licenses here for more information.

The VIKAMINE system has been sucessfully applied in several projects by domain specialists, for example, for

visual analytics, by applying powerful, intuitive and simple to use visualizations,
knowledge discovery, by mining interesting subgroups for selected target parameters,
knowledge refinement, by mining deviating/interesting subgroups that are applied for tuning the knowledge base, or for
quality control, by tracing quality parameters and mining explaining reasons for quality variations.

For several application examples, see Publications below.

If you have a question concerning VIKAMINE please let us know and send us an e-mail: contact@vikamine.org
We will then get back to you shortly.

Code/Downloads

The source code of VIKAMINE is available in the SVN repository on the sourceforge project website, see VIKAMINE SVN.

The current release for VIKAMINE can be found on the sourceforge project page which is located here.

License: VIKAMINE is licensed under the GNU Library or Lesser General Public License (LGPL). For further information on the LGPL we refer to the homepage of the Free Software Foundation.

Documentation

You can find a short tutorial on using VIKAMINE here.
A quickstart screencast is available here.
A Busy Developers' Guide to the VIKAMINE Kernel.
Please also see some Implementation Examples.
The API for the current release can be found here (JavaDoc).

Furthermore, you might want to have a look at the publications below, especially on the paper VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics..

Publications

Selected publications about VIKAMINE:

Atzmueller, M.; Lemmerich, F. VIKAMINE - Open-Source Subgroup Discovery, Pattern Mining, and Analytics. Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer, Heidelberg, 2012.
Atzmueller, M., Knowledge-Intensive Subgroup Mining, Diski 307, March 2007, IOS Press, 214 pp., ISBN: 978-1-58603-726-0, 2007
Atzmueller, M.; Puppe, F. Semi-Automatic Visual Subgroup Mining using VIKAMINE , Journal of Universal Computer Science (JUCS), Special Issue on Visual Data Mining, 11 (11), pp. 1752-1765, 2005
Atzmueller, M.; Lemmerich, F. Fast Subgroup Discovery for Continuous Target Concepts, Proc. 18th International Symposium on Methodologies for Intelligent Systems (ISMIS), 2009
Atzmueller, M.; Puppe, F.; Buscher, H.-P. Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery , Proceedings of the 19^th International Joint Conference on Artificial Intelligence (IJCAI-05), pp. 647-652, 2005
Atzmueller, M.; Baumeister, J.; Hemsing, A.: Richter E. J.; Puppe, F. Subgroup Mining for Interactive Knowledge Refinement , Proceedings of the10^th Conference on Artificial Intelligence in Medicine (AIME 05),LNAI 3581, pp. 453-462, 2005
Atzmueller, M.; Puppe, F.; Buscher, H.-P. Profiling Examiners using Intelligent Subgroup Mining , Proceedings of the 10^th Workshop on Intelligent Data Analysis in Medicine and Pharmacology, pp. 46-51, 2005

Further selected publications about subgroup discovery:

Atzmueller, M. Subgroup Discovery - Advanced Review. WIREs Data Mining Knowl Discov, 5:35-49, 2015.
Atzmueller, M. Subgroup Discovery. Künstliche Intelligenz, 4:52--53, 2005.
Martin Atzmueller and Frank Puppe. SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery. In: Knowledge Discovery in Databases: PKDD 2006, LNCS 4213, pp. 6-17, Springer Verlag, 2006.