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Incremental Constrained Clustering by Minimal Weighted Modification

Authors Aymeric Beauchamp , Thi-Bich-Hanh Dao , Samir Loudni , Christel Vrain

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Author Details

Aymeric Beauchamp
  • University of Orléans, INSA Centre Val de Loire, LIFO EA 4022, France
Thi-Bich-Hanh Dao
  • University of Orléans, INSA Centre Val de Loire, LIFO EA 4022, France
Samir Loudni
  • TASC (LS2N-CNRS), IMT Atlantique, France, GREYC, University of Caen Normandy, France
Christel Vrain
  • University of Orléans, INSA Centre Val de Loire, LIFO EA 4022, France

Funding

This work was supported by the French national research project HERELLES under grant agreement ANR-20-CE23-0022.

Acknowledgements

The authors want to thank the anonymous reviewers for their comments and suggestions which helped to improve this paper.

Cite AsGet BibTex

Aymeric Beauchamp, Thi-Bich-Hanh Dao, Samir Loudni, and Christel Vrain. Incremental Constrained Clustering by Minimal Weighted Modification. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 10:1-10:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.10

Abstract

Clustering is a well-known task in Data Mining that aims at grouping data instances according to their similarity. It is an exploratory and unsupervised task whose results depend on many parameters, often requiring the expert to iterate several times before satisfaction. Constrained clustering has been introduced for better modeling the expectations of the expert. Nevertheless constrained clustering is not yet sufficient since it usually requires the constraints to be given before the clustering process. In this paper we address a more general problem that aims at modeling the exploratory clustering process, through a sequence of clustering modifications where expert constraints are added on the fly. We present an incremental constrained clustering framework integrating active query strategies and a Constraint Programming model to fit the expert expectations while preserving the stability of the partition, so that the expert can understand the process and apprehend its impact. Our model supports instance and group-level constraints, which can be relaxed. Experiments on reference datasets and a case study related to the analysis of satellite image time series show the relevance of our framework.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Semi-supervised learning settings
Keywords
  • Incremental constrained clustering
  • Constrained optimization problem
  • User feedback

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