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Equitable Conceptual Clustering using OWA operator

Abstract : We propose an equitable conceptual clustering approach based on multi-agent optimization. In the context of conceptual clustering, each cluster is represented by an agent having its own satisfaction and 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 datasets and on instances coming from real application ERP show that our approach efficiently finds clusterings of consistently high quality.
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https://hal.archives-ouvertes.fr/hal-01709850
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Submitted on : Thursday, March 1, 2018 - 5:44:53 PM
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  • HAL Id : hal-01709850, version 1

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Noureddine Aribi, Abdelkader Ouali, Yahia Lebbah, Samir Loudni. Equitable Conceptual Clustering using OWA operator. 22nd Pacific-Asia Conference, PAKDD 2018, Jun 2018, Melbourne, Australia. pp.465-477. ⟨hal-01709850⟩

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