Rule-Based Constraint Elicitation For Active Constraint-Incremental Clustering
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.
Authors
- Thi-Bich-Hanh Dao (ORCID: https://orcid.org/0000-0002-2740-6954)
- Christel Vrain (ORCID: https://orcid.org/0000-0003-3307-0753)
- Aymeric Beauchamp (ORCID: https://orcid.org/0000-0001-9894-7056)
- Samir Loudni
Institutions
- Centre National de la Recherche Scientifique (FR)
- Laboratoire des Sciences du Numérique de Nantes (FR)
- IMT Atlantique (FR)
- Services déconcentrés d'appui à la recherche Val de Loire (FR)
- GREYC (FR)
- Institut National des Sciences Appliquées Centre Val de Loire (FR)
- Laboratoire d'Informatique Fondamentale d'Orléans
- Nantes Université (FR)
- Université de Caen Normandie (FR)
Publication Details
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1109/ictai62512.2024.00117
- Primary Topic
- Advanced Clustering Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00