  This paper addresses the problem of learning the concept of "propagation" in
the pretopology theoretical formalism. Our proposal is first to define the
pseudo-closure operator (modeling the propagation concept) as a logical
combination of neighborhoods. We show that learning such an operator lapses
into the Multiple Instance (MI) framework, where the learning process is
performed on bags of instances instead of individual instances. Though this
framework is well suited for this task, its use for learning a pretopological
space leads to a set of bags exponential in size. To overcome this issue we
thus propose a learning method based on a low estimation of the bags covered by
a concept under construction. As an experiment, percolation processes (forest
fires typically) are simulated and the corresponding propagation models are
learned based on a subset of observations. It reveals that the proposed MI
approach is significantly more efficient on the task of propagation model
recognition than existing methods.
