Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have pr...Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have proposed a new rough set approach based on dominance relation to handle the problems. The concept of dominance matrix is put forward and the dominance function is constructed to compute the minimal decision rules which are more general and applicable than the ones induced by the classical rough set theory. In addition, the methodology of simplification is presented to eliminate the redundancy in the rule set.展开更多
The concepts of Rough Decision Support System (RDSS) and equivalence matrix are introduced in this paper. Based on a rough attribute vector tree (RAVT) method, two kinds of matrix computation algorithms — Recursive M...The concepts of Rough Decision Support System (RDSS) and equivalence matrix are introduced in this paper. Based on a rough attribute vector tree (RAVT) method, two kinds of matrix computation algorithms — Recursive Matrix Computation (RMC) and Parallel Matrix Computation (PMC) are proposed for rules extraction, attributes reduction and data cleaning finished synchronously. The algorithms emphasize the practicability and efficiency of rules generation. A case study of PMC is analyzed, and a comparison experiment of RMC algorithm shows that it is feasible and efficient for data mining and knowledge-discovery in RDSS.展开更多
To give a better understanding of the morphological features of rock fracture surfaces within the framework of fractal geometry, the fractal characters of the rough surfaces in rock are analyzed according to the vario...To give a better understanding of the morphological features of rock fracture surfaces within the framework of fractal geometry, the fractal characters of the rough surfaces in rock are analyzed according to the variogram method. The study elaborates the significance of the geometric parameters-fractal dimension D and the intercept A on a log-log plot to the surface structure. Investigation extends to the anisotropy and heterogeneity of rock fracture surfaces, and the scale effect on the fractal estimation. The present study indicates that fractal dimension alone may not be sufficient to characterize the surface roughness of rock Joints. A reliable estimation should take into account the combination of D and A.展开更多
This paper discusses on the detection of outliers by hybridizing Rough_Outlier Algorithm with Negative Association Rules. An optimization algorithm named Binary Particle Swarm Optimization is used to improve the compu...This paper discusses on the detection of outliers by hybridizing Rough_Outlier Algorithm with Negative Association Rules. An optimization algorithm named Binary Particle Swarm Optimization is used to improve the computation of Non_Reduct in order to detect outliers.By using Binary PSO algorithm, the rules generated from Rough_Outliers algorithm is optimized, giving significant outliers object detected. The detection ofoutliers process is then enhanced by hybridizing it with Negative Association Rules. Frequent and Infrequent item sets from outlier rules are generated. Results show that the hybrid Rough_Negative algorithm is able to uncover meaningful knowledge of outliers from the frequent and infrequent item sets. These knowledge can then be used by experts in their field of domain for better decision making.展开更多
基金Project (2022YFC2904501) supported by the National Key Research and Development Program of ChinaProject (2022YFC2905105) supported by the National Key Research Center and Development Program of the 14th Five-year Plan,ChinaProject (52122406) supported by the National Natural Science Foundation of China。
文摘Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have proposed a new rough set approach based on dominance relation to handle the problems. The concept of dominance matrix is put forward and the dominance function is constructed to compute the minimal decision rules which are more general and applicable than the ones induced by the classical rough set theory. In addition, the methodology of simplification is presented to eliminate the redundancy in the rule set.
文摘The concepts of Rough Decision Support System (RDSS) and equivalence matrix are introduced in this paper. Based on a rough attribute vector tree (RAVT) method, two kinds of matrix computation algorithms — Recursive Matrix Computation (RMC) and Parallel Matrix Computation (PMC) are proposed for rules extraction, attributes reduction and data cleaning finished synchronously. The algorithms emphasize the practicability and efficiency of rules generation. A case study of PMC is analyzed, and a comparison experiment of RMC algorithm shows that it is feasible and efficient for data mining and knowledge-discovery in RDSS.
文摘To give a better understanding of the morphological features of rock fracture surfaces within the framework of fractal geometry, the fractal characters of the rough surfaces in rock are analyzed according to the variogram method. The study elaborates the significance of the geometric parameters-fractal dimension D and the intercept A on a log-log plot to the surface structure. Investigation extends to the anisotropy and heterogeneity of rock fracture surfaces, and the scale effect on the fractal estimation. The present study indicates that fractal dimension alone may not be sufficient to characterize the surface roughness of rock Joints. A reliable estimation should take into account the combination of D and A.
文摘This paper discusses on the detection of outliers by hybridizing Rough_Outlier Algorithm with Negative Association Rules. An optimization algorithm named Binary Particle Swarm Optimization is used to improve the computation of Non_Reduct in order to detect outliers.By using Binary PSO algorithm, the rules generated from Rough_Outliers algorithm is optimized, giving significant outliers object detected. The detection ofoutliers process is then enhanced by hybridizing it with Negative Association Rules. Frequent and Infrequent item sets from outlier rules are generated. Results show that the hybrid Rough_Negative algorithm is able to uncover meaningful knowledge of outliers from the frequent and infrequent item sets. These knowledge can then be used by experts in their field of domain for better decision making.
