Subgroup discovery toolkit for Orange
Please also check the subgroup discovery toolkit for RapidMiner here.
The subgroup discovery toolkit for Orange implements three algorithms for subgroup discovery: SD, CN2-SD and Apriori-SD, two visualization methods: the BAR and the ROC visualization and six evaluation measures for subgroup discovery.
It is distributed free under GPL and can be downloaded from this web page.
One needs to have Orange installed and working. Orange is available for download here. In order for the PMML functionality to work, one also needs to have pyxml installed. The version for Python 2.6 for Linux users is available here and for Windows users here.
Subgroup discovery windows setup file for Orange for Python 2.6: SubgroupDiscoveryToolkit-1.1.3.win32.exe (released on August 8, 2011).
The Subgroup discovery toolkit can be downloaded SubgroupDiscoveryToolkit-1.1.3.zip and unziped in the folder c:\Python26\Lib\site-packages\Orange\OrangeWidgets\
An old version for Orange 1.0 can be found here.
- Double-click the downloaded file. When/If prompted to choose the Python folder, choose Python2.6.
- Run OrangeCanvas: A new "tab" named SubgroupDiscovery should appear in Orange canvas and the tool is ready to be used.
Subgroup discovery schema 1:
Subgroup BAR vizualization from Schema 1:
Subgroup ROC vizualization from Schema 1:
Scatter plot vizualization from Schema 1:
Subgroup discovery evaluation schema:
Subgroup evaluation from the evaluation schema:
Petra Kralj Novak(1), Nada Lavrač(1)
Implemented by Petra Kralj Novak(1) with the help of the Orange team(2).
Vid Podpečan(1), Grega Podlesek, Robert Ravnik, Miha Rojko and Anže Vavpetič(1) also contributed in the implementation.
- Department of Knowledge Technologies,
Jožef Stefan Institute
Jamova 39, 1000 Ljubljana, Slovenia
- Artificial Intelligence Laboratory,
Faculty of Computer and Information Science,
University of Ljubljana
Tržaška 25, 1000 Ljubljana, Slovenia
Petra Kralj Novak
phone: +386 1 477 36 57