Archimedes AI Researcher receives Best Paper Runner-Up at Discovery Science 2024

Archimedes is proud to announce that John Pavlopoulos (Assistant Professor at Athens University of Economics and Business and Lead Researcher at Archimedes) latest research, "Revisiting Silhouette Aggregation", was recognized as Best Paper Runner Up at Discovery Science 2024 in Pisa. Prof. Pavlopoulos, in collaboration with A. Likas and G. Vardakas from the Department of Computer Science & Engineering of the University of Ioannina, uncovered a powerful yet overlooked method of evaluating clustering solutions using the Silhouette Coefficient—particularly advantageous for datasets with cluster imbalance. 

Their paper further introduced a novel sampling technique, essential for large datasets, enhancing robustness and reliability in clustering analysis.

Through synthetic examples, the study demonstrate that micro-averaging is highly sensitive to cluster imbalance whereas macro-averaging is considerably more robust. Its analysis further reveals that existing uniform sub-sampling techniques negatively impact the robustness of micro- and macro-averaged Silhouette scores in the presence of cluster imbalance.

To address this issue, the proposes a novel per-cluster sampling strategy that maintains the robustness of the macro-averaged Silhouette score. This method is evaluated on eight real-world datasets, where both micro- and macro-averaged Silhouette scores are compared in clustering tasks, revealing the potential benefits of considering both aggregation strategies for more reliable clustering evaluation.

Overall, this work highlights a refined toolset for robust clustering assessment, particularly valuable for large or imbalanced datasets.

Many congratulations to Prof. John Pavlopoulos and his collaborators for this impactful contribution to clustering evaluation and robust data analysis!