Une variante pondérée de K-means adaptée aux données textuelles

This article proposes a weighted variant of the K-means algorithm, specifically adapted to textual data. The algorithm aims to improve clustering performance and interpretability by assigning weights to the most discriminating features for each cluster. Experimental results show that the weighted K-means algorithm outperforms traditional clustering methods on a variety of text datasets.