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 has been studied extensively in data mining research. Temporal and Spatio-Temporal Data Mining: Association Patterns and Applications 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: Association Patterns and Applications 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: flow patterns and generalized spatio-temporal patterns. This innovative book 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.