- If you make use of this iCVI MATLAB Toolbox please cite the following paper
[1] L. E. Brito da Silva, N. M. Melton, and D. C. Wunsch II, "Incremental Cluster Validity Indices for Hard Partitions: Extensions and Comparative Study," ArXiv e-prints, Feb 2019, arXiv:1902.06711v1 [cs.LG].
and refer to this Applied Computational Intelligence Laboratory (ACIL) Github repository as
[2] L. E. Brito da Silva, N. M. Melton, and D. C. Wunsch II, "iCVI MATLAB Toolbox," 2019.
[Online].
Available: https://github.com/ACIL-Group/iCVI-toolbox
- The data sets used in the experiments section of the paper mentioned above are available at:
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UCI machine learning repository:
http://archive.ics.uci.edu/ml -
Fundamental Clustering Problems Suite (FCPS):
https://www.uni-marburg.de/fb12/arbeitsgruppen/datenbionik/data?language_sync=1 -
Clustering basic benchmark and Other clustering datasets:
http://cs.uef.fi/sipu/datasets -
MATLAB processed data sets:
http://www.cad.zju.edu.cn/home/dengcai/Data/MLData.html
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The figures depicting the behaviors of the iCVIs when correctly, under- and over-partitioning the data sets used in the experiments of the paper mentioned above can be downloaded from:
https://drive.google.com/drive/folders/1WzGnBLgvGOkyf8z2UeeePpx8ywmuGZJB?usp=sharing -
The "main_incremental_CVIs_example.m" and "main_batch_CVIs_example.m" files contain examples of usage of the iCVI MATLAB Toolbox.