  Co-clustering is a specific type of clustering that addresses the problem of
finding groups of objects without necessarily considering all attributes. This
technique has shown to have more consistent results in high-dimensional sparse
data than traditional clustering. In trajectory co-clustering, the methods
found in the literature have two main limitations: first, the space and time
dimensions have to be constrained by user-defined thresholds; second, elements
(trajectory points) are clustered ignoring the trajectory sequence, assuming
that the points are independent among them. To address the limitations above,
we propose a new trajectory co-clustering method for mining semantic trajectory
co-clusters. It simultaneously clusters the trajectories and their elements
taking into account the order in which they appear. This new method uses the
element frequency to identify candidate co-clusters. Besides, it uses an
objective cost function that automatically drives the co-clustering process,
avoiding the need for constraining dimensions. We evaluate the proposed
approach using real-world a publicly available dataset. The experimental
results show that our proposal finds frequent and meaningful contiguous
sequences revealing mobility patterns, thereby the most relevant elements.
