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

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CB-SMoT: Speed-based clustering (Palma 2008)• Clusters single trajectories based on the speed variation:low speed important placeCB-SMoT: Speed-based clustering (Palma 2008)Jurere09-12Unknown stopInput: Trajectory samplesSpeed variationminTimeOutput: stops and movesStep 1: find clustersStep 2: Add semantics to eachclusterIbisH.13-14FloripaS16-172.1: If intersects α during ∆tα stop α2.2: If no intersectionduring ∆t unknown stop
CB-SMoT: Speed-based clustering (Palma 2008)Unknown Stops (CB-SMOT)T 1same unknown stopT 2another unknown stopCB-SMoT: Speed-based clustering (Palma 2008)Can Find Clusters Inside Buildingsp 1p 6p 7p 11t6= 10:10AMt7= 10:32AM
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 and 22: TraClass Algorithm (Lee 2008)Second
Page 23 and 24: TraOD - Trajectory Outlier Detectio
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: IB-SMoT (Alvares 2007a)A stop of a
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

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

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