A comparison of clustering methods for biogeography with fossil datasets
- Published
- Accepted
- Subject Areas
- Biogeography, Computational Biology, Ecology, Mathematical Biology, Paleontology
- Keywords
- cluster analysis, ecological similarity, biogeography, Adjusted Rand Index, palaeoecology
- Copyright
- © 2016 Vavrek
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Cite this article
- 2016. A comparison of clustering methods for biogeography with fossil datasets. PeerJ PrePrints 4:e1693v1 https://doi.org/10.7287/peerj.preprints.1693v1
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 Language is described. This method, Non-Euclidean Relational Clustering (NERC), creates distinct clusters by dividing the data set in order to maximize the average similarity within each cluster, identifying clusters in which each data point is on average more similar to those within its own group than to those in any other group. While all the methods performed well with clearly differentiated and well-sampled datasets, when data are less than ideal the linkage methods perform poorly compared to non-Euclidean based k-means and the NERC method. Based on this analysis, Unweighted Pair Group Method with Arithmetic Mean and neighbor joining methods are less reliable with incomplete datasets like those found in palaeobiological analyses, and the k-means and NERC methods should be used in their place.
Author Comment
This version has been accepted for publication at PeerJ after peer review.
Supplemental Information
R code for the NERC function
This is the full R Statistical Language code for the NERC function.
R source code
All R code used for data analysis and figure creation in the manuscript.