{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["4"],"submitter":["Vavrek MJ"],"pubmed_abstract":["Cluster analysis is one of the most commonly used methods in palaeoecological studies, particularly in studies investigating biogeographic patterns. Although a number of different clustering methods are widely used, the approach and underlying assumptions of many of these methods are quite different. For example, methods may be hierarchical or non-hierarchical in their approaches, and may use Euclidean distance or non-Euclidean indices to cluster the data. In order to assess the effectiveness of the different clustering methods as compared to one another, a simulation was designed that could assess each method over a range of both cluster distinctiveness and sampling intensity. Additionally, a non-hierarchical, non-Euclidean, iterative clustering method implemented in the R Statistical Lan"],"journal":["PeerJ"],"pagination":["e1720"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4782735"],"repository":["biostudies-literature"],"pubmed_title":["A comparison of clustering methods for biogeography with fossil datasets."],"pmcid":["PMC4782735"],"pubmed_authors":["Vavrek MJ"],"additional_accession":[]},"is_claimable":false,"name":"A comparison of clustering methods for biogeography with fossil datasets.","description":"Cluster analysis is one of the most commonly used methods in palaeoecological studies, particularly in studies investigating biogeographic patterns. Although a number of different clustering methods are widely used, the approach and underlying assumptions of many of these methods are quite different. For example, methods may be hierarchical or non-hierarchical in their approaches, and may use Euclidean distance or non-Euclidean indices to cluster the data. In order to assess the effectiveness of the different clustering methods as compared to one another, a simulation was designed that could assess each method over a range of both cluster distinctiveness and sampling intensity. Additionally, a non-hierarchical, non-Euclidean, iterative clustering method implemented in the R Statistical Lan","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016","modification":"2025-04-05T10:27:54.137Z","creation":"2019-03-27T02:10:39Z"},"accession":"S-EPMC4782735","cross_references":{"pubmed":["26966658"],"doi":["10.7717/peerj.1720"]}}