TY - DATA T1 - Performance of classical k-means, fdakma, and kmlShape in case of small and medium datasets (mean value ± standard deviation). PY - 2016/06/03 AU - Christophe Genolini AU - René Ecochard AU - Mamoun Benghezal AU - Tarak Driss AU - Sandrine Andrieu AU - Fabien Subtil UR - https://plos.figshare.com/articles/dataset/Performance_of_classical_k-means_fdakma_and_kmlShape_in_case_of_small_and_medium_datasets_mean_value_standard_deviation_/3414475 DO - 10.1371/journal.pone.0150738.t001 L4 - https://ndownloader.figshare.com/files/5345275 KW - Alzheimer disease KW - Cluster Longitudinal Data KW - application KW - luteinizing hormone KW - Efficient Method KW - methods group trajectories KW - Shapes Background Longitudinal data KW - data partitioning algorithm KW - data simplification procedures KW - time points KW - methods group N2 - Performance of classical k-means, fdakma, and kmlShape in case of small and medium datasets (mean value ± standard deviation). ER -