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Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results.

, , и . Int. J. Geogr. Inf. Sci., 22 (4): 361-386 (2008)

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Luis Otávio Maddalozzo

Otávio Prado

Jorge Otávio Trierweiler

Werner Luis

Pia Luislampe

 

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Discovering Heterogeneous Subsequences for Trajectory Classification., , , , и . CoRR, (2019)ST-DMQL: A Semantic Trajectory Data Mining Query Language., , и . Int. J. Geogr. Inf. Sci., 23 (10): 1245-1276 (2009)Criteria for the Analysis of Coordination in Multi-agent Applications., и . COORDINATION, том 2315 из Lecture Notes in Computer Science, стр. 158-165. Springer, (2002)A model for enriching trajectories with semantic geographical information., , , , , и . GIS, стр. 22. ACM, (2007)Filtering Frequent Spatial Patterns with Qualitative Spatial Reasoning., , и . ICDE Workshops, стр. 527-535. IEEE Computer Society, (2007)GEOARM: an Interoperable Framework to Improve Geographic Data Preprocessing and Spatial Association Rule Mining., , и . SEKE, стр. 79-84. (2006)Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints., , , , , и . ICDM, стр. 813-817. IEEE Computer Society, (2006)Evaluating the effect of compressing algorithms for trajectory similarity and classification problems., , , , , и . GeoInformatica, 25 (4): 679-711 (2021)MASTER: A multiple aspect view on trajectories., , , , , , , и . Trans. GIS, 23 (4): 805-822 (2019)Unusual behavior detection and object ranking from movement trajectories in target regions., , и . Int. J. Geogr. Inf. Sci., 31 (2): 364-386 (2017)