Communication Dans Un Congrès Année : 2024

Rule-Based Constraint Elicitation For Active Constraint-Incremental Clustering

Résumé

Constrained clustering algorithms integrate user knowledge as constraints in the clustering process to guide it towards a desired outcome. When interacting with users, it is essential to quickly ask simple questions to identify informative constraints that will efficiently enhance an initial partition. We propose a new active query strategy for incremental clustering that translates user feedback into interpretable decision rules and identifies relevant points for queries using rule-based heuristics Experiments on benchmark datasets highlight the benefits of our new approach, making it suitable for real-world applications.

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Dates et versions

hal-04932712 , version 1 (06-02-2025)

Identifiants

Citer

Aymeric Beauchamp, Thi-Bich-Hanh Dao, Samir Loudni, Christel Vrain. Rule-Based Constraint Elicitation For Active Constraint-Incremental Clustering. ICTAI 2024: IEEE 36th International Conference on Tools with Artificial Intelligence, Oct 2024, Herndon, United States. pp.798-805, ⟨10.1109/ICTAI62512.2024.00117⟩. ⟨hal-04932712⟩
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