Logiciels

AlphaGalicia

Le logiciel AlphaGalicia est une extension de la plateforme Galicia, que nous avons développée afin d’implémenter les notions de « projection Alpha » et de « règles Alpha » présentées dans Ventos et Soldano (2005). Dans ce cadre, ce logiciel permet de construire des treillis de Galois projetés par fusion de treillis (Soldano et al., 2010). Il permet en outre la visualisation et l’exploration des ces treillis.

http://lipn.univ-paris13.fr/~champesme/alphabetagalicia/

  • Contact : @
  • Réferences :
    • H. Soldano, V. Ventos, M. Champesme, and D. Forge. Incremental construction of alpha lattices and association rules. In Proc. of the 14th Int. Conf. on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2010), LNCS 6277: 351-360. Springer, 2010.
    • V. Ventos et H. Soldano. Treillis de Galois Alpha. Revue d’intelligence artificielle RSTI série RIA 19(4-5): 799-227, 2005.

Clustering4Ever – C4E

Its  Big Data Clustering Library (API) gathering clustering algorithms and quality indexes in Scala and Spark/Scala. Don’t hesitate to ask questions or make recommendations in our Gitter
https://gitter.im/Clustering4Ever/Lobby

https://github.com/Clustering4Ever/Clustering4Ever

  • Contact : Gael Beck, Florent Forest, Mustapha LEBBAH et Hanane Azzag
  • Laboratoire d’origine : LIPN
  • Réferences :

LEAR

Lear est un système d’apprentissage de concepts relationnels.
 Il apprend un ensemble de règles d’ordre 1 à partir d’exemples ambigus représentés par des théories clausales.

http://lipn.univ-paris13.fr/~bouthinon/softwares/lear/lear.html

  • Contact : @
  • Réferences :
    • D. Bouthinon, H. Soldano, and V. Ventos. Concept learning from (very) ambiguous examples. In Proc. of the 6th Int. Conf. on Machine Learning and Data Mining in Pattern Recognition (MLDM 2009), LNCS 5632: 465-478. Springer, 2009.

MinerLC

Extraction de motifs clos dans les graphes attribués.

https://lipn.univ-paris13.fr/MinerLC/

  • Contact : Henry Soldano, Dominique Bouthinon, Guillaume Santini
  • Laboratoire d’origine : LIPN CNRS UMR 7030
  • Réferences :
    • Hub-Authority Cores and Attributed Directed Network Mining
      Henry Soldano and Guillaume Santini and Dominique Bouthinon and Emmanuel Lazega
      IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI 2017), Boston, MA, USA, IEEE Computer Society, 2017
    • Local knowledge discovery in attributed graphs
      Henry Soldano and Guillaume Santini and Dominique Bouthinon
      27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2015), Vietri sul Mare, Italy, 250–257, Anna Esposito, IEEE Computer Society, 2015
    • Graph abstraction for closed pattern mining in attributed network
      Soldano Henry and Santini Guillaume
      European Conference in Artificial Intelligence (ECAI), 849–854, Torsten Schaub and Gerhard Friedrich and Barry O’Sullivan, IOS Press, Frontiers in Artificial Intelligence and Applications, 263, 2014

Spartakus (Spark-clustering-notebook)

Its introduces somme clustering algorithms and describes its current implementation in the software using since 2012 Spark and Spark-notebook. This notebook has a dual purpose: teaching and research.
https://lipn.univ-paris13.fr/bigdata

https://github.com/Spark-clustering-notebook/coliseum/wiki

  • Contact : Mustapha LEBBAH et Hanane Azzag
  • Laboratoire d’origine : LIPN CNRS UMR 7030, Machine Learning team
  • Réferences :
    • Tarn Duong, Gael Beck, Hanene Azzag, Mustapha Lebbah. Nearest neighbour estimators of density derivatives, with application to mean shift clustering. Pattern Recognition Letters (2016). http://dx.doi.org/10.1016/j.patrec.2016.06.021
    • Mohammed Ghesmoune, Mustapha Lebbah, and Hanane Azzag. state-of-the-art on clustering data stream (invited paper). Big Data Analytics journal, 2016
    • Tugdual Sarazin, Mustapha Lebbah, and Hanane Azzag. Biclustering using spark- mapreduce. In 2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014, pages 58–60, 2014.