  We present a variant of the well sounded Expectation-Maximization Clustering
algorithm that is constrained to generate partitions of the input space into
high and low values. The motivation of splitting input variables into high and
low values is to favour the semantic interpretation of the final clustering.
The Expectation-Maximization binary Clustering is specially useful when a
bimodal conditional distribution of the variables is expected or at least when
a binary discretization of the input space is deemed meaningful. Furthermore,
the algorithm deals with the reliability of the input data such that the larger
their uncertainty the less their role in the final clustering. We show here its
suitability for behavioural annotation of movement trajectories. However, it
can be considered as a general purpose algorithm for the clustering or
segmentation of multivariate data or temporal series.
