  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, or also using
clustering methods for continuous data on a multidimensional scaling
representation of the distances. These methods are conceptually very different
and can therefore not easily be compared theoretically. We compare their
performance with a comprehensive simulation study based on models for species
distributions.
  This has been accepted for publication in Ferreira, J., Bekker, A., Arashi,
M. and Chen, D. (eds.) Innovations in multivariate statistical modelling:
navigating theoretical and multidisciplinary domains, Springer Emerging Topics
in Statistics and Biostatistics.
