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

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Trajectory Sequential PatternsFrequent Sequential Patterns (Cao, 2005)Three main steps:1. Transforms each trajectory in a line with several segments A distance tolerance measure is defined (similar to buffer) All trajectory points inside this distanceare summarized in one segment2. Similar segments are grouped Similarity is based on the angle and the spatial lenght of the segmentSegments with same angle and length havetheir distance checked based on a given distance d thresholdFrom the resultant groups, a medium segment is createdFrom this segment a region (buffer) is created3. Frequent sequences of regions are computedconsidering a minSup threshold
T-Patterns (Giannotti, 2007)Sequential Trajectory Pattern MiningConsider both space and timeObjective is to describe frequent movementConsidering visited regions of interestDuring movements and the duration of movementsSteps:1. Compute or find regions of interest, based on dense spatialregions (no time is considered)2. Select trajectories that intersect two or more regions in asequence, annotating travel time from one region to another3. Compute sequences of regions visited in same time intervalsT-Patterns (Giannotti, 2007)Fix a set of pre-defined regionsBACMap each (x,y) of the trajectory to its regiontimeSample pattern:A⎯ 20min.⎯⎯→B
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: Co-Location Patterns (Cao 2006)Co-l
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 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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