Repository for Bridge-Aware Clustering algorithm, developed in python3.

pip install -r requirements.txt

conda env create -f environment.yml

The BorderPeeling implementation is obtained from authors' official repository:

https://github.com/nadavbar/BorderPeelingClustering

Minor changes were made to adapt the original source code to python3.

The DADC implementation is obtained from authors' official repository:

https://github.com/JianguoChen2015/DADC

Minor changes were made: plotting functionalities and export information as csv files were disabled.

The DenMune clustering algorithm was installed via pip install denmune command.

The authors' implementation is available at the following GitHub repository: https://github.com/egy1st/denmune-clustering-algorithm.

The 25 synthetic dataset were downloaded from

https://github.com/deric/clustering-benchmark

The datasets are stored in datasets folder in .arff format.

• comparisons.py: computes the adjusted rand index scores for each of the considered techniques and perform the statistical tests. Generates figures and a final report;
• statistical_tests.py: performs the Friedman and Nemenyi statistical tests given the input .csv file;
• utils.py: contains utility functions;
• generate_arrow_images.py: generates images with connectivity graph;
• plot_bridges_figures.py: generates plots containing datasets, clusters, bridges and outliers;
• grid_search.py: performs the grid search and save results using pickle;
• run_dadc.py: run the DADC algorithm only;

@ARTICLE{bridge_clustering,
  author={Colomba, Luca and Cagliero, Luca and Garza, Paolo},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={Density-Based Clustering by Means of Bridge Point Identification}, 
  year={2023},
  volume={35},
  number={11},
  pages={11274-11287},
  doi={10.1109/TKDE.2022.3232315}
}