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

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ReferencesVerhein, F. and Chawla, S. (2006). Mining spatio-temporal association rules, sources, sinks,stationary regions and thoroughfares in object mobility databases. In Lee, M.- L., Tan,K.-L., and Wuwongse, V., editors, DASFAA, volume 3882 of Lecture Notes in ComputerScience, pages 187–201. Springer.Gudmundsson, J. and van Kreveld, M. J. (2006). Computing longest duration flocks intrajectory data. In [de By and Nittel 2006], pages 35–42.Gudmundsson, J., van Kreveld, M. J., and Speckmann, B. (2007). Efficient detection ofpatterns in 2d trajectories of moving points. GeoInformatica, 11(2):195–215.Hwang, S.-Y., Liu, Y.-H., Chiu, J.-K., and Lim, E.-P. (2005). Mining mobile group patterns: Atrajectory-based approach. In Ho, T. B., Cheung, D. W.-L., and Liu, H., editors, PAKDD,volume 3518 of Lecture Notes in Computer Science, pages 713–718. Springer.Cao, H., Mamoulis, N., and Cheung, D. W. (2006). Discovery of collocation episodes inspatiotemporal data. In ICDM, pages 823–827. IEEE Computer Society.Zhenhui Li, Jae-Gil Lee, Xiaolei Li, Jiawei Han: Incremental Clustering for Trajectories.DASFAA (2) 2010: 32-46More...Huiping Cao, Nikos Mamoulis, David W. Cheung: Discovery of Periodic Patterns in SpatiotemporalSequences. IEEE Trans. Knowl. Data Eng. 19(4): 453-467 (2007)Panos Kalnis, Nikos Mamoulis, Spiridon Bakiras: On Discovering Moving Clusters in SpatiotemporalData. SSTD, 364-381 (2005)Florian Verhein, Sanjay Chawla: Mining spatio-temporal patterns in object mobility databases.Data Min. Knowl. Discov. 16(1): 5-38 (2008)Florian Verhein, Sanjay Chawla: Mining Spatio-temporal Association Rules, Sources, Sinks,Stationary Regions and Thoroughfares in Object Mobility Databases. DASFAA, 187-201(2006)Cao, H., Mamoulis, N., and Cheung, D. W. (2005). Mining frequent spatio-temporal sequentialpatterns. In ICDM ’05: Proceedings of the Fifth IEEE International Conference on Data Mining,pages 82–89, Washington, DC, USA. IEEE Computer Society.Jae-Gil Lee, Jiawei Han, Xiaolei Li, and Hector Gonzalez, “TraClass: Trajectory ClassificationUsing Hierarchical Region-Based and Trajectory-Based Clustering”, Proc. 2008 Int. Conf.on Very Large Data Base (VLDB'08), Auckland, New Zealand, Aug. 2008.Jae-Gil Lee, Jiawei Han, and Xiaolei Li, "Trajectory Outlier Detection: A Partition-and-DetectFramework", Proc. 2008 Int. Conf. on Data Engineering (ICDE'08), Cancun, Mexico, April2008.
Semantic-based Spatio-temporal Data MiningMethodsSemantic Trajectory Data MiningThe main idea is to enrich trajectories with domainsemantic information in preprocessing stepsThis task can be done using data miningApply data mining as a second stepMining is on semantic rich trajectories
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: SummaryThese data mining approaches
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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