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- W3195825241 abstract "AbstractPresence-absence data is defined by vectors or matrices of zeroes and ones, where the ones usually indicate a “presence” in a certain place. Presence-absence data occur, for example, when investigating geographical species distributions, genetic information, or the occurrence of certain terms in texts. There are many applications for clustering such data; one example is to find so-called biotic elements, i.e., groups of species that tend to occur together geographically. Presence-absence data can be clustered in various ways, namely, using a latent class mixture approach with local independence, distance-based hierarchical clustering with the Jaccard distance, K-modes, a density-based approach, or also using clustering methods for continuous data on a multidimensional scaling representation of the distances. These methods are conceptually very different from each other, and can therefore not easily be compared theoretically. We compare their performance with a comprehensive simulation study based on models for species distributions.KeywordsMultidimensional scalingBiogeographyCluster analysisSimulation studyBenchmarkingJaccard’s distance" @default.
- W3195825241 created "2021-08-30" @default.
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- W3195825241 date "2022-01-01" @default.
- W3195825241 modified "2023-10-15" @default.
- W3195825241 title "A Comparison of Different Clustering Approaches for High-Dimensional Presence-Absence Data" @default.
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- W3195825241 doi "https://doi.org/10.1007/978-3-031-13971-0_13" @default.
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