Semi-supervised learning using multiple clusterings with limited labeled data
This is the companion web page for our paper titled "Semi-supervised learning using multiple clusterings with limited labeled data".
This paper has been accepted for publication to Information Sciences, Elsevier.
If you have any comments/questions regarding the research work or software, please feel free to contact us.
Datasets used for the experiments and results
The artificial datasets used in the paper are available here (ARFF format). They were originally proposed by Julia Handl.
The remote sensing dataset is available here (ARFF format).
The full results obtained on the datasets during the experiments are available:
- for the UCI datasets
- for the ten 2d-4c datasets
- for the ten 2d-10c datasets
- for the ten 10d-4c datasets
The software
The software is developed in Java, it uses the Weka classification library.
The package is licensed under a GPLv3 (license in the Jar files) : donwload the soure code.
To run the program just execute: java -jar Slemc.jar sample.arff in a console.
Last update : May 2016