Tutorial on Spatial and Spatio-Temporal Data Mining Part II ... - UFSC

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Trajectory Outlier Detection• The objective is to find trajectories that have different behaviorin relation to other trajectories• For instance:– A fishing vessel that has a behaviour differentfrom other fishing vessels in the same area– A hurricane that may change behaviour in certainparts of its trajectory– Cars or pedestrians with suspishious behaviour43TraOD - Trajectory Outlier Detection (Lee 2008)• Partition trajectories into subtrajectories• Compare subtrajectories based on:– distance and length• If a subtrajectory is not close to othertrajectories for a minimal lenght– It is an outlier44
TraOD - Trajectory Outlier Detection (Lee 2008)• Example:– Looking to the whole trajectory, TR 3 is not detected asan outlier since its overall behavior is similar toneigbouhr trajectories• Looking at the subtrajectories, T3 can be an outlierTR 5TR 1TR 4 TR3TR2An outlying sub-trajectory45TraOD - Trajectory Outlier Detection (Lee 2008)Two phases: partitioning and detectionTR 5TR 4 TR3TRTR 21(1) PartitionA set of trajectoriesA set of trajectory partitions(2) DetectTR 3An outlierOutlying trajectory partitions46
Page 1 and 2: Tutorial on Spatia
Page 3 and 4: The Wireless A Explosão Explosion
Page 5 and 6: Mobility Data AnalysisAnimal Migrat
Page 7 and 8: Trajectories: Overall Characteristi
Page 9 and 10: Mining Trajectories : Frequent patt
Page 11 and 12: Geometry-based Trajectory Data Mini
Page 13 and 14: Relative Motion Patterns (Laube 200
Page 15 and 16: Co-Location Patterns (Cao 2006)Co-l
Page 17 and 18: T-Patterns (Giannotti, 2007)Sequent
Page 19 and 20: TraClass Algorithm (Lee 2008)Two ma
Page 21: TraClass Algorithm (Lee 2008)Second
Page 25 and 26: SummaryThese data mining approaches
Page 27 and 28: Semantic-based Spatio-temporal Data
Page 29 and 30: DJ-Cluster (Zhou 2007)DJ-Cluster is
Page 31 and 32: Semantic TrajectoriesA semantic tra
Page 33 and 34: IB-SMoT (Alvares 2007a)A stop of a
Page 35 and 36: CB-SMoT: Speed-based clustering (Pa
Page 37 and 38: Semantic Rich Trajectories (Transpo
Page 39 and 40: STOPS at Multiple-Granularities (Bo
Page 41 and 42: Multiple-Granularity Semantic Traje
Page 43 and 44: Weka-STDMWeka-STDM
Page 45 and 46: Athena (Baglioni 2009)Semantic-rich
Page 47 and 48: Pattern Interpretation (Rebeca 2010
Page 49 and 50: ReferencesManso, J. A. ; TIMES, V.
Page 51 and 52: Challenges and Open Issues in Spati

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