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Accélération de k-means par pré-calcul dynamique d'agrégats., , and . EGC, volume E-35 of RNTI, page 255-260. Éditions RNTI, (2019)MPT: Suite Tools to Support Performance Tuning in NoSQL Systems., , , and . ICEIS (1), page 127-134. SciTePress, (2018)DECWA: Density-Based Clustering using Wasserstein Distance., , , and . CIKM, page 2005-2008. ACM, (2020)Impact of similarity measures on clustering mixed data., , , , and . SSDBM, page 9:1-9:12. ACM, (2022)DECWA : Density-Based Clustering using Wasserstein Distance., , , and . CoRR, (2023)New Partition-based and Density-based approaches for improving clustering. (Nouvelles approches basées sur la partition et la densité pour l'amélioration du clustering).. University of Toulouse-Jean Jaurès, France, (2021)KD-means: Clustering Method for Massive Data based on KD-tree., , and . DOLAP, volume 2572 of CEUR Workshop Proceedings, page 26-35. CEUR-WS.org, (2020)A New Accurate Clustering Approach for Detecting Different Densities in High Dimensional Data., , , and . DaWaK, volume 12925 of Lecture Notes in Computer Science, page 167-179. Springer, (2021)Classification de surfaces dans une image hyperspectrale urbaine par réseau de neurones pour la qualité de l'air., , , , , , and . APIA, page 24-27. (2023)k-means Improvement by Dynamic Pre-aggregates., , and . ICEIS (1), page 133-140. SciTePress, (2019)