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Equitable Conceptual Clustering Using OWA Operator

  • Noureddine Aribi
  • Abdelkader Ouali
  • Yahia Lebbah
  • Samir Loudni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)

Abstract

We propose an equitable conceptual clustering approach based on multi-agent optimization, where each cluster is represented by an agent having its own satisfaction. The problem consists in finding the best cumulative satisfaction while emphasizing a fair compromise between all individual agents. The fairness goal is achieved using an equitable formulation of the Ordered Weighted Averages (OWA) operator. Experiments performed on UCI and ERP datasets show that our approach efficiently finds clusterings of consistently high quality.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Noureddine Aribi
    • 1
  • Abdelkader Ouali
    • 2
  • Yahia Lebbah
    • 1
  • Samir Loudni
    • 2
  1. 1.Lab. LITIO, University of Oran 1OranAlgeria
  2. 2.Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYCCaenFrance

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