Interactive Visual Clustering of Large Collections of Trajectories

One of the most common operations in exploration and analysis of
various kinds of data is clustering, i.e. discovery and interpretation
of groups of objects having similar properties and/or behaviors. In
clustering, objects are often treated as points in multi-dimensional
space of properties. However, structurally complex objects, such
as trajectories of moving entities and other kinds of spatiotemporal
data, cannot be adequately represented in this manner.
Such data require sophisticated and computationally intensive
clustering algorithms, which are very hard to scale effectively to
large datasets not fitting in the computer main memory. We
propose an approach to extracting meaningful clusters from large
databases by combining clustering and classification, which are
driven by a human analyst through an interactive visual interface.