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Temporal and spatio-temporal data mining

By: Hsu, Wynne.
Contributor(s): Lee, Mong Li | Wang, Junmei.
Publisher: Hershey IGI Publishing 2008Description: x, 280 p.ISBN: 9781599043876.Subject(s): Data mining | Temporal databasesDDC classification: 005.74 Summary: The recent surge of interest in spatio-temporal databases has resulted in numerous advances, such as: modeling, indexing and querying of moving objects and spatio-temporal data. Aside from this, rule mining in spatial databases and temporal databases have been studied extensively in data mining research. Temporal and-Temporal Data Mining examines the problem of mining topological patterns in spatio-temporal databases by imposing the temporal constraints into the process of mining spatial collocation patterns. Temporal and Spatio-Temporal Data Mining presents probable solutions when discovering the spatial sequence patterns by incorporating the spatial information into the sequence of patterns, and introduces two new classes of spatial sequence patterns, called flow patterns and generalized spatio-temporal patterns. This innovative title addresses different scenarios when finding complex relationships in spatio-temporal data by modeling them as graphs, giving readers a comprehensive synopsis on two successful partition-based algorithms designed by the authors. (Source: Back cover of the book)
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The recent surge of interest in spatio-temporal databases has resulted in numerous advances, such as: modeling, indexing and querying of moving objects and spatio-temporal data. Aside from this, rule mining in spatial databases and temporal databases have been studied extensively in data mining research. Temporal and-Temporal Data Mining examines the problem of mining topological patterns in spatio-temporal databases by imposing the temporal constraints into the process of mining spatial collocation patterns. Temporal and Spatio-Temporal Data Mining presents probable solutions when discovering the spatial sequence patterns by incorporating the spatial information into the sequence of patterns, and introduces two new classes of spatial sequence patterns, called flow patterns and generalized spatio-temporal patterns. This innovative title addresses different scenarios when finding complex relationships in spatio-temporal data by modeling them as graphs, giving readers a comprehensive synopsis on two successful partition-based algorithms designed by the authors. (Source: Back cover of the book)

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