| Summary: | This thesis investigates the applicability of a data mining algorithm for automatic pattern
discovery widely used for conventional databases, called Apriori, to a new domain - 2D
motion trajectory data. This is one the first attempts to analyze motion trajectory data, in
the data mining style, i.e., to develop methods for automatic finding of interesting
patterns or rules in the object motion trajectories. While our focus is on the application to
the hockey game analysis, similar methods could also be used in the area of video
surveillance, for sport game strategies, or more generally in geographic applications.
More specifically, our focus is on the discovery of the hockey game patterns that contain
frequent motion trajectories of the hockey players, where the frequency is defined with
respect to a motion trajectory similarity measure. Furthermore, the patterns relate motion
of the players of the same or opposing teams, which should be correlated according to
their roles in the game. We design and implement a system that discovers such patterns,
and test its effectiveness and efficiency on a real life and semi-randomly generated data
set. Our effectiveness tests tend to prove the right choice of the motion trajectory
similarity measure, and the validity of the algorithm. Our tests also include a comparison
of using the Apriori algorithm, with a semi-naive algorithm, proving the importance of
using Apriori, which outperforms the semi-naive algorithm for various choices of
parameters and data sizes.
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