Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted freque...Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.展开更多
The distance-based outlier detection method detects the implied outliers by calculating the distance of the points in the dataset, but the computational complexity is particularly high when processing multidimensional...The distance-based outlier detection method detects the implied outliers by calculating the distance of the points in the dataset, but the computational complexity is particularly high when processing multidimensional datasets. In addition, the traditional outlier detection method does not consider the frequency of subsets occurrence, thus, the detected outliers do not fit the definition of outliers (i.e., rarely appearing). The pattern mining-based outlier detection approaches have solved this problem, but the importance of each pattern is not taken into account in outlier detection process, so the detected outliers cannot truly reflect some actual situation. Aimed at these problems, a two-phase minimal weighted rare pattern mining-based outlier detection approach, called MWRPM-Outlier, is proposed to effectively detect outliers on the weight data stream. In particular, a method called MWRPM is proposed in the pattern mining phase to fast mine the minimal weighted rare patterns, and then two deviation factors are defined in outlier detection phase to measure the abnormal degree of each transaction on the weight data stream. Experimental results show that the proposed MWRPM-Outlier approach has excellent performance in outlier detection and MWRPM approach outperforms in weighted rare pattern mining.展开更多
Water-conserving mining is an effective way to alleviate the contradiction between fragile ecological environment and high-intensity coal mining in the arid and semi-arid region of northwest China. It needs to conside...Water-conserving mining is an effective way to alleviate the contradiction between fragile ecological environment and high-intensity coal mining in the arid and semi-arid region of northwest China. It needs to consider the engineering and geological conditions, hydrogeological conditions and mining methods of coal seams. From the three aspects, this paper systematically analyzes the influencing factors and establishes an identification model with multi-level structures. The model includes three primary factors (including the engineering and geological conditions, hydrogeological conditions and mining methods), nine secondary factors (including overlying strata thickness, aquiclude, mining parameters and etc.), sixteen third-tier factors (including the faults, aquiclude thickness and effective mining height and etc.) and twelve fourth-tier factors (including the fault throw exp on ent, aquiclude permeability and coal pillar sizes and etc.). On the basis, the analytic hierarchy process is used to build the judgment matrix and obtain the weight of each influencing factor. The results indicate that the overlying strata thickness, aquiclude and effective mining height are the most import a nt factors among the primary factors of engineering and geological conditions, hydrogeological conditions and mining methods, respectively. The research results could provide theoretical references for the water-conserving mining of coal resources in northwest China.展开更多
The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain facto...The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.展开更多
Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone...Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.展开更多
The fast high-efficiency inspection for mining subsidence of mine area is a reliable way for forecasting accident and evaluating losing expense.In order to monitor mining subsidence of exploitation mine efficiently,Li...The fast high-efficiency inspection for mining subsidence of mine area is a reliable way for forecasting accident and evaluating losing expense.In order to monitor mining subsidence of exploitation mine efficiently,LiDAR data were used and a novel strip division method was brought forward based on separating-treatment theory,which divided the mass of discrete three-dimensional point cloud data into a series of parallel strips and reduced the dimension in each strip.Polynomial fitting algorithm based on the adaptive weights,which located in the range of the strip,was used for classification complex terrain data of mine-area.The results show that LiDAR datamation can be greatly reduced.In the mean time,the time spending for calculation is shortened,and computational complexity is simplified.Therefore,high-efficiency terrain classification of LiDAR point cloud method can be great beneficial to monitoring environment of mine area.展开更多
Based on fuzzy characteristic of dicision-making thought, matrix of priority relation has been introduced and blurrized. A kind of fuzzy method, which is to determine the index weight on multi-objective decision makin...Based on fuzzy characteristic of dicision-making thought, matrix of priority relation has been introduced and blurrized. A kind of fuzzy method, which is to determine the index weight on multi-objective decision making, has been put forward by means of the sequence root method for analysis of hierarchical process (AHP). Using this method an example which is to define the index weigbt on multi-objective decision making in thc scheme optimization of mine design has been given.展开更多
文摘Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.
基金supported by Fundamental Research Funds for the Central Universities (No. 2018XD004)
文摘The distance-based outlier detection method detects the implied outliers by calculating the distance of the points in the dataset, but the computational complexity is particularly high when processing multidimensional datasets. In addition, the traditional outlier detection method does not consider the frequency of subsets occurrence, thus, the detected outliers do not fit the definition of outliers (i.e., rarely appearing). The pattern mining-based outlier detection approaches have solved this problem, but the importance of each pattern is not taken into account in outlier detection process, so the detected outliers cannot truly reflect some actual situation. Aimed at these problems, a two-phase minimal weighted rare pattern mining-based outlier detection approach, called MWRPM-Outlier, is proposed to effectively detect outliers on the weight data stream. In particular, a method called MWRPM is proposed in the pattern mining phase to fast mine the minimal weighted rare patterns, and then two deviation factors are defined in outlier detection phase to measure the abnormal degree of each transaction on the weight data stream. Experimental results show that the proposed MWRPM-Outlier approach has excellent performance in outlier detection and MWRPM approach outperforms in weighted rare pattern mining.
基金the National Key Basic Research Program of China (973 Program)(grant number 2015CB251600)the National Natural Science Foundation of China (51874280)+1 种基金the Open Project of Key Laboratory of Mine Geological Hazards Mechanism and Control (grant number KF2017-02)the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Water-conserving mining is an effective way to alleviate the contradiction between fragile ecological environment and high-intensity coal mining in the arid and semi-arid region of northwest China. It needs to consider the engineering and geological conditions, hydrogeological conditions and mining methods of coal seams. From the three aspects, this paper systematically analyzes the influencing factors and establishes an identification model with multi-level structures. The model includes three primary factors (including the engineering and geological conditions, hydrogeological conditions and mining methods), nine secondary factors (including overlying strata thickness, aquiclude, mining parameters and etc.), sixteen third-tier factors (including the faults, aquiclude thickness and effective mining height and etc.) and twelve fourth-tier factors (including the fault throw exp on ent, aquiclude permeability and coal pillar sizes and etc.). On the basis, the analytic hierarchy process is used to build the judgment matrix and obtain the weight of each influencing factor. The results indicate that the overlying strata thickness, aquiclude and effective mining height are the most import a nt factors among the primary factors of engineering and geological conditions, hydrogeological conditions and mining methods, respectively. The research results could provide theoretical references for the water-conserving mining of coal resources in northwest China.
文摘The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.
基金supported in part by the National Key R&D Program of China (2023YFA1011601)the Major Key Project of PCL, China (PCL2023AS7-1)+3 种基金in part by the National Natural Science Foundation of China (U21A20478, 62106224, 92267203)in part by the Science and Technology Major Project of Guangzhou (202007030006)in part by the Major Key Project of PCL (PCL2021A09)in part by the Guangzhou Science and Technology Plan Project (2024A04J3749)。
文摘Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.
基金Project(2011CB707102)supported by the National Basic Research Program of ChinaProjects(41001302,40901220)supported by the National Natural Science Foundation of China+2 种基金Project(200903190)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(20090450305)supported by the China Postdoctoral Science FoundationProject(122025)supported by the Fok Ying Tong Education Foundation,China
文摘The fast high-efficiency inspection for mining subsidence of mine area is a reliable way for forecasting accident and evaluating losing expense.In order to monitor mining subsidence of exploitation mine efficiently,LiDAR data were used and a novel strip division method was brought forward based on separating-treatment theory,which divided the mass of discrete three-dimensional point cloud data into a series of parallel strips and reduced the dimension in each strip.Polynomial fitting algorithm based on the adaptive weights,which located in the range of the strip,was used for classification complex terrain data of mine-area.The results show that LiDAR datamation can be greatly reduced.In the mean time,the time spending for calculation is shortened,and computational complexity is simplified.Therefore,high-efficiency terrain classification of LiDAR point cloud method can be great beneficial to monitoring environment of mine area.
文摘Based on fuzzy characteristic of dicision-making thought, matrix of priority relation has been introduced and blurrized. A kind of fuzzy method, which is to determine the index weight on multi-objective decision making, has been put forward by means of the sequence root method for analysis of hierarchical process (AHP). Using this method an example which is to define the index weigbt on multi-objective decision making in thc scheme optimization of mine design has been given.
