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Local consistency is a condition used in constraint satisfaction problems (CSPs) to simplify problems by ensuring that small subsets of variables comply with their constraints without considering the entire network. It helps in reducing the search space and making CSPs more efficient by iteratively applying Local consistency checks to prune incompatible variable assignments.
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Pattern formation refers to the process by which a homogenous system develops structured, often repetitive, configurations due to the interactions of its components. This phenomenon is observed in various disciplines, including biology, chemistry, and physics, and is driven by mechanisms such as reaction-diffusion, symmetry breaking, and self-organization.
Concept
Spatial-temporal data mining involves extracting meaningful patterns and knowledge from data that has both spatial and temporal dimensions, enabling insights into phenomena that change over space and time. This field is crucial for applications such as climate change analysis, urban planning, and transportation management, where understanding the dynamics of data in both space and time is essential.
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