The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates ...The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.展开更多
Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method(HASM) is proposed, and HASM_Big is developed to handle very...Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method(HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem(LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations.A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model.Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion,HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.展开更多
文摘The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41541010, 41701456, 41421001, 41590840 & 91425304)the Key Programs of the Chinese Academy of Sciences (Grant No. QYZDY-SSW-DQC007)the Cultivate Project of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (Grant No. TSYJS03)
文摘Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method(HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem(LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations.A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model.Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion,HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.
