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

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Geometric Patterns X Semantic Patterns (Bogorny 2008)Geometric PatternRTPRTPCCT 3T 2T 1HCCT 2T 3T 4HT 4T 1HHotelRRestaurantTP TouristicPlaceSemantic trajectory Pattern(a) Hotel to Restaurant, passing by CC(b) go to Cinema, passing by CCGeometric Patterns X Semantic Patterns (Bogorny 2008)There is very little or no semantics in most DM approaches fortrajectoriesConsequence:• Patterns are purely geometrical• Difficult to interpret from the user’s point of view• Do not discover semantic patterns,which can be independent of spatial location
DJ-Cluster (Zhou 2007)DJ-Cluster is a variation of DBSCANFocus relies on performance issuesObjective: find interesting places of individual trajectoriesClusters are computed from a SET of trajectories of the same objectTime is not consideredA Conceptual View on Trajectories (Spaccapietra 2008)A trajectory is a spatio-temporal thing (an object) thathas generic featuresgeneric: application independenthas semantic featuressemantic: application dependentA trajectory is more than a moving object
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: Semantic-based Spatio-temporal Data
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

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

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