A comparison of different clustering approaches for high-dimensional presence-absence data

Presence-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.