A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness
Abstract— Discovering trajectory patterns is shown to be very useful in learning interactions between moving objects. Many types of trajectory patterns have been proposed in the literature, but previous methods were developed for only a speciﬁc type of trajectory patterns. This limitation could make pattern discovery tedious and inefﬁcient since users typically do not know which types of trajectory patterns are hidden in their data sets. Our main observation is that many trajectory patterns can be arranged according to the strength of temporal constraints. In this paper, we propose a unifying framework of mining trajectory patterns of various temporal tightness, which we call unifying trajectory patterns (UT-patterns). This framework consists of two phases: initial pattern discovery and granularity adjustment. A set of initial patterns are discovered in the ﬁrst phase, and their granularities < Final Year Projects 2016 > are adjusted by split and merge to detect other types in the second phase.
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