Constrained Clustering Problems: New Optimization Algorithms - Archive ouverte HAL
Communication Dans Un Congrès Année : 2021

Constrained Clustering Problems: New Optimization Algorithms

Hatem Ibn Khedher
Makhlouf Hadji
Mohamed Ibn Khedher
  • Fonction : Auteur
  • PersonId : 1224892
Selma Khebbache
  • Fonction : Auteur
  • PersonId : 1002083

Résumé

Constrained clustering problems are often considered in massive data clustering and analysis. They are used in modeling various issues in anomaly detection, classification, systems' misbehaviour, etc. In this paper, we focus on generalizing the K-Means clustering approach when involving linear constraints on the clusters' size. Indeed, to avoid local optimum clustering solutions which consists in empty clusters or clusters with few points, we propose linear integer programming approaches based on relaxation and rounding techniques to cope with scalability issues. We show the efficiency of the new proposed approach, and assess its performance using five data-sets from different domains.
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hal-04619077 , version 1 (20-06-2024)

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  • HAL Id : hal-04619077 , version 1

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Hatem Ibn Khedher, Makhlouf Hadji, Mohamed Ibn Khedher, Selma Khebbache. Constrained Clustering Problems: New Optimization Algorithms. 20th International Conference on Artificial Intelligence and Soft Computing (ICAISC), Jun 2021, Zakopan, Poland. ⟨hal-04619077⟩
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