Abstract:
The mobile communication technologies
penetrate our society and wireless network to detect
the movement of people to generate large amount of
data mobility including mobile phone call records
and Global Positioning System (GPS) traces which
can be characterized as big trajectory data. The
remarkable analytical strength of the massive data
collection trajectory can help to show the complexity
of human mobility. The knowledge discovery process
is addressed on some of the fundamental issues of
mobility analysts such as the ways people move. In
this work, the problem of determining the number of
groups and the members of the trajectory nodes
within the group from big trajectory data are
considered. A framework for clustering moving
objects from big trajectory data is designed.
Additionally, a distance based clustering algorithm to
specify the number of groups and their identity are
proposed. Finally, the proposed methods are
practically evaluated using real Geolife dataset.
