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This project is the implementation of a small sequential fair division game over a graph.

Développement d'une carte web interactive du glacier d'Argentière pour le projet Saussure

OJS plugin to conceal review type.

Depot des TP M1 de l'UE Variabilité Climatique et Environnementale

Wiki du groupe Tal for distant languages

Code et configurations pour le modèle elmer/ice

Project IAB@R

Some helps for ASI student with jupyter

This project develops a comprehensive benchmarking framework for time-series clustering, addressing the lack of standardized guidelines for selecting appropriate approach for clustering tasks. The framework derives and evaluates 15 clustering pipelines comprising multiple well-known clustering techniques and diverse distance metrics, with the constraint of using the same number of clusters for all pipelines to ensure comparability and consistency in the evaluation process. The procedure for standardizing clustering labels, generating ensemble outcomes, and incorporating a stability score is introduced to provide a comprehensive and rigorous evaluation of clustering pipelines. The challenge posed by arbitrary label assignments across different pipelines is resolved through a standardization process that aligns the clusters for meaningful comparison. The ensemble approach mitigates inconsistencies in clustering results and addresses the limitations of traditional clustering validity metrics, leading to more stable and reliable groupings. Additionally, the inclusion of a novel stability score adds a critical layer of evaluation, enabling the identification of the most consistent and accurate clustering outcomes. The results highlight limitations of traditional quality metrics, while showcasing the strong performance for various pipelines with more than 90% of similarity with ensemble results. This would aid in informed selection of the best pipelines for specific applications. The framework is further discussed in the context of optimizing clustering for demand response strategies in smart grids, highlighting its real-world relevance. To support transparency and reproducibility, the study provides open-source code for validating and applying it to various time-series datasets, offering a robust tool for benchmarking clustering across domains.

Portage de l'application MACAO, basé sur Hugo et SurveyJS