Clustering with EDAs

This repository is a container for Estimation of Distribution Algorithms (EDAs) that perform the data mining task of clustering.

All algorithms are published, as denoted in Section Citation.

AffEDA is an EDA for enhancing the quality of Affinity Propagation, which is a clustering algorithm per se.

Clus-EDA performs clustering through the use of medoids, cluster representatives that are coincident with data objects (as opposed to centroids, which are hypothetical data objects).

PASCAL performs a density-based clustering using a Minimum Spanning Tree (MST). It produces a MST from the set of data objects and then proceeds to remove edges from this structure. Two objects connected by an edge are considered to belong to the same cluster.

Citation

If you use any of the algorithms here implemented in your work, please cite the corresponding authors:

AffEDA

Santana, Roberto, Pedro Larrañaga, and José A. Lozano. "Learning factorizations in estimation of distribution algorithms using affinity propagation." Evolutionary Computation 18.4 (2010): 515-546.

@article{santana2010learning,
  title={Learning factorizations in estimation of distribution algorithms using affinity propagation},
  author={Santana, Roberto and Larra{\~n}aga, Pedro and Lozano, Jos{\'e} A},
  journal={Evolutionary Computation},
  volume={18},
  number={4},
  pages={515--546},
  year={2010},
  publisher={MIT Press}
}

Clus-EDA

Cagnini, Henry EL, et al. "Medoid-based data clustering with estimation of distribution algorithms." Proceedings of the 31st Annual ACM Symposium on Applied Computing. ACM, 2016.

@inproceedings{cagnini2016medoid,
  title={Medoid-based data clustering with estimation of distribution algorithms},
  author={Cagnini, Henry EL and Barros, Rodrigo C and Quevedo, Christian V and Basgalupp, M{\'a}rcio P},
  booktitle={Proceedings of the 31st Annual ACM Symposium on Applied Computing},
  pages={112--115},
  year={2016},
  organization={ACM}
}

PASCAL

Henry E. L. Cagnini, Rodrigo C. Barros. "PASCAL: An EDA for parameterless shape-independent clustering". Proceedings of the 2016 IEEE Congress on Evolutionary Computation. IEEE, 2016.

@inproceedings{DBLP:conf/cec/CagniniB16,
  author    = {Henry E. L. Cagnini and
               Rodrigo C. Barros},
  title     = {{PASCAL:} An {EDA} for parameterless shape-independent clustering},
  booktitle = {{IEEE} Congress on Evolutionary Computation, {CEC} 2016, Vancouver,
               BC, Canada, July 24-29, 2016},
  pages     = {3433--3440},
  year      = {2016},
  url       = {http://dx.doi.org/10.1109/CEC.2016.7744224},
  doi       = {10.1109/CEC.2016.7744224},
  timestamp = {Thu, 24 Nov 2016 20:39:06 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/cec/CagniniB16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}