In this paper, a new algorithm HCOUNT ＋ is proposed to find frequent items over data stream based on the HCOUNT algorithm. The new algorithm adopts aided measures to improve the precision of HCOUNT greatly. In additi...In this paper, a new algorithm HCOUNT ＋ is proposed to find frequent items over data stream based on the HCOUNT algorithm. The new algorithm adopts aided measures to improve the precision of HCOUNT greatly. In addition,HCOUNT ＋ is introduced to time critical applications and a novel sliding windows-based algorithm SL-HCOUNT ＋ is proposed to mine the most frequent items occurring recently.This algorithm uses limited memory （nB · （1 ＋α） · e/ε·In（-M/lnρ）（α〈1） counters）, requires constant processing time per packet （only （1＋α） · ln（-M/lnρ（α〈1）） counters are updated）, makes only one pass over the streaming data,and is shown to work well in the experimental results.展开更多
A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial partic...A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.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
Future airspace operations require the integration of various types of comprehensive combat forces,which belong to systematic combat.At present,integrated systems are oriented to the single platform system.Systematic ...Future airspace operations require the integration of various types of comprehensive combat forces,which belong to systematic combat.At present,integrated systems are oriented to the single platform system.Systematic integrated mission system is more complex,which not only considers the utility and efficiency of the single platform,but also considers multi-platform collaboration.Therefore,it is more complicated and difficult to obtain requirements and design mission system architecture,which requires multiple design scenarios.Due to the large amount of data of the scenarios model,the efficiency of manual analysis is too low,so data mining method is needed to analyze the scenarios data.However,traditional data mining methods cannot simultaneously mine item location information and utility occupancy.This paper proposes an algorithm:SHUO-FI,to mine utility occupancy threshold under specified distance constraints and a new pruning strategy based on support and maximum utility occupancy constraints.Compared with other algorithms,it is found that the proposed algorithm has higher efficiency.Since the algorithm considers the location,weight,profit,utility and other parameters of the project,it can be better applied in the field of forward design MBSE than the previous algorithm.Finally,the algorithm is applied to the actual coordination of manned and unmanned aircraft model.By data mining the utility,profit and real-time position of each operational unit at each time,the optimal operational function scheduling mode under the same operational mission can be obtained.展开更多
文摘In this paper, a new algorithm HCOUNT ＋ is proposed to find frequent items over data stream based on the HCOUNT algorithm. The new algorithm adopts aided measures to improve the precision of HCOUNT greatly. In addition,HCOUNT ＋ is introduced to time critical applications and a novel sliding windows-based algorithm SL-HCOUNT ＋ is proposed to mine the most frequent items occurring recently.This algorithm uses limited memory （nB · （1 ＋α） · e/ε·In（-M/lnρ）（α〈1） counters）, requires constant processing time per packet （only （1＋α） · ln（-M/lnρ（α〈1）） counters are updated）, makes only one pass over the streaming data,and is shown to work well in the experimental results.
文摘A novel binary particle swarm optimization for frequent item sets mining from high-dimensional dataset(BPSO-HD) was proposed, where two improvements were joined. Firstly, the dimensionality reduction of initial particles was designed to ensure the reasonable initial fitness, and then, the dynamically dimensionality cutting of dataset was built to decrease the search space. Based on four high-dimensional datasets, BPSO-HD was compared with Apriori to test its reliability, and was compared with the ordinary BPSO and quantum swarm evolutionary(QSE) to prove its advantages. The experiments show that the results given by BPSO-HD is reliable and better than the results generated by BPSO and QSE.
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.
基金sponsored by National Program on Key Basic Research Project(2014CB744903)National Natural Science Foundation of China(61673270)+5 种基金Natural Science Foundation of Shanghai(20ZR1427800)New Young Teachers Launch Program of Shanghai Jiaotong University(20X100040036)Shanghai Pujiang Program(16PJD028)Shanghai Industrial Strengthening Project(GYQJ-2017-5-08)Shanghai Science and Technology Committee Research Project(17DZ1204304)Shanghai Engineering Research Center of Civil Aircraft Flight Testing.
文摘Future airspace operations require the integration of various types of comprehensive combat forces,which belong to systematic combat.At present,integrated systems are oriented to the single platform system.Systematic integrated mission system is more complex,which not only considers the utility and efficiency of the single platform,but also considers multi-platform collaboration.Therefore,it is more complicated and difficult to obtain requirements and design mission system architecture,which requires multiple design scenarios.Due to the large amount of data of the scenarios model,the efficiency of manual analysis is too low,so data mining method is needed to analyze the scenarios data.However,traditional data mining methods cannot simultaneously mine item location information and utility occupancy.This paper proposes an algorithm:SHUO-FI,to mine utility occupancy threshold under specified distance constraints and a new pruning strategy based on support and maximum utility occupancy constraints.Compared with other algorithms,it is found that the proposed algorithm has higher efficiency.Since the algorithm considers the location,weight,profit,utility and other parameters of the project,it can be better applied in the field of forward design MBSE than the previous algorithm.Finally,the algorithm is applied to the actual coordination of manned and unmanned aircraft model.By data mining the utility,profit and real-time position of each operational unit at each time,the optimal operational function scheduling mode under the same operational mission can be obtained.
