04-Trajectory Data Mining-Trajectory Patternmining

A) Clusters of segments gments B) Micro-clusters C) Macro-clusters B) Micro-clusters C) Macro-clusters
[2] Z. Li, J. Lee, X.Li, and J. Han. Incremental Clustering for Trajectories. DASFAA 2010
– Clustering-based methods [1]
• Detect stay points from trajectories • Clustering stay points into regions • Apply PrefixSpan or CloseSpan to find sequential patterns
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E) MDL-based
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A) MBR Distance
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B) Trajectory-Hausdorff Distance
[1] J. G. Lee, J. Han, and K. Y. Whang. Trajectory clustering: A partition-and-group framework. SIGMOD 2007
• Free spaces or in a road network setting
Mining Sequential Patterns from Trajectories
• In a free space
– Line simplification-based method
• Using line simplification algorithm to compress a trajectory • Group simplified segments based on distance (without considering temporal gaps)
•
Convoy
– captures generic trajectory pattern of any shape – by employing the density-based clustering – requires a group of objects to be density-connected during k consecutive time points
•
Swarm
– a cluster of objects lasting for at least k (possibly non-consecutive) timestamps
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Managing Recent Trajectories
Compression Segmentation
TD
Trajectory Preprocessing Map-Matching Stay Point Detection Noise Filtering
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CF
Matrix
Spatial Trajectories
• Li et al. [2]
– new data will only affect the local area where the new data were received rather than the far-away areas – Incremental clustering algorithm
• A certain number of moving objects traveling a common sequence of locations in a similar time interval
• Applications
– – – – – travel recommendation, life pattern understanding, next location prediction, estimating user similarity trajectory compression
B) Gathering
– The shape or density of a group – The number of objects in a group – The duration of a pattern
Moving Together Patterns
• Flock
– a group of objects that travel together within a disc of some user-specified size for at least k consecutive timestamps – The pre-defined circular shape may not well describe the shape of a group in reality
• Group similar trajectories into clusters
– To find representative paths or – common trends shared by different moving objects
• In free spaces
– Distance between two entire trajectories – Distance between segments of trajectories
– – – – – Flock Convoy Swarm Traveling companion Gathering
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• Differences
A) Flock, convoy and swarm
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• How to define a location
– Exact match: Check-in, road segment ID – Approximate match: spatial closeness, GPS trajectories
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Trajectory Clustering
• Micro-and-Macro-clustering framework
– First find mirco-clusters of trajectory segments – Then group micro-clusters into macro-clusters
Trajectory Pattern Mining
• • • • Moving Together Patterns Trajectory Clustering Sequential Patterns Periodic Patterns
Moving Together Patterns
• Discover a group of objects moving together for a certain time period • Patterns
Root
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Trajectory Data Mining
Dr. Yu Zheng
Lead Researcher, Microsoft Research Chair Professor at Shanghai Jiao Tong University Editor-in-Chief of ACM Trans. Intelligent Systems and Technology
A) Flock, convoy and swarm
B) Gathering
Convoy and swarm need to load entire trajectories into memory for a pattern mining!
Moving Together Patterns
o1 o2 o 3 o 4
Trajectory Pattern Mining
• • • • Moving Together Patterns Trajectory Clustering Sequential Patterns Periodic Patterns
Mining Sequential Patterns from Trajectories
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Paradigm of Trajectory Data Mining
Uncertainty Privacy Preserving Reducing Uncertainty
Traj. Pattern Mining Moving Freq. Seq. Together Patterns Patterns Periodic Clustering Patterns