  This paper reports on ongoing research investigating more expressive
approaches to spatial-temporal trajectory clustering. Spatial-temporal data is
increasingly becoming universal as a result of widespread use of GPS and mobile
devices, which makes mining and predictive analyses based on trajectories a
critical activity in many domains. Trajectory analysis methods based on
clustering techniques heavily often rely on a similarity definition to properly
provide insights. However, although trajectories are currently described in
terms of its two dimensions (space and time), their representation is limited
in that it is not expressive enough to capture, in a combined way, the
structure of space and time as well as the contextual and semantic trajectory
properties. Moreover, the massive amounts of available trajectory data make
trajectory mining and analyses very challenging. In this paper, we briefly
discuss (i) an improved trajectory representation that takes into consideration
space-time structures, context and semantic properties of trajectories; (ii)
new forms of relations between the dimensions of a pair of trajectories; and
(iii) big data approaches that can be used to develop a novel spatial-temporal
clustering framework.
