TY - JOUR AU - Hulot, Audrey AU - Chiquet, Julien AU - Jaffrézic, Florence AU - Rigaill, Guillem PY - 2020 DA - 2020/03/20 TI - Fast tree aggregation for consensus hierarchical clustering JO - BMC Bioinformatics SP - 120 VL - 21 IS - 1 AB - In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-020-3453-6 DO - 10.1186/s12859-020-3453-6 ID - Hulot2020 ER -