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
由于传统的煤层气产能分析算法存在影响因素不够全面,运行效率低和人为设置聚类参数缺乏说服力的问题。因此,该文在煤层气产能分类的基础上,对分类结果进行回溯,挖掘煤层气产能影响因素的规律,将基于密度聚类算法(Density-Based Spatial...由于传统的煤层气产能分析算法存在影响因素不够全面,运行效率低和人为设置聚类参数缺乏说服力的问题。因此,该文在煤层气产能分类的基础上,对分类结果进行回溯,挖掘煤层气产能影响因素的规律,将基于密度聚类算法(Density-Based Spatial Clustering of Application with Noise,DBSCAN)与频繁模式增长算法(Frequent-Pattern Growth,FP-Growth)关联度分析算法优化结合,提出新的基于DBSCAN的FP-growth煤层气产能分析模型,找出影响煤层气产能的关键因素及其对应的参数范围。该文是深度学习与煤层气开发交叉学科的应用与研究,致力于煤层气产能分析评价体系的构建,为提高煤层气单井产气量,提升措施选井的决策效率有积极影响。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
云计算为大数据提供了展示和共享的平台.为了防止隐私泄露,这些数据中往往包含人为添加的不确定因素,如何挖掘这些不确定数据是大数据共享亟待解决的问题.在用于共享的大数据中,不确定数据通过对精确数据的泛化处理来实现,具有均匀分布...云计算为大数据提供了展示和共享的平台.为了防止隐私泄露,这些数据中往往包含人为添加的不确定因素,如何挖掘这些不确定数据是大数据共享亟待解决的问题.在用于共享的大数据中,不确定数据通过对精确数据的泛化处理来实现,具有均匀分布特性,这一特性不利于精确查询,但可为关联规则的挖掘提供便利条件.首先,依据泛化值之间可能的相交或包含关系,将泛化值进行分层聚类,为了保存与不确定数据集挖掘相关的重要信息,给出了构建不确定频繁模式树的算法,在此基础上,提出了频繁项集挖掘子算法(data mining algorithm for uncertain frequent item-sets,UFI-DM)和关联规则生成子算法(algorithm for generating association rules,GAR),分别用于挖掘频繁项集和生成关联规则,最后,通过理论分析和实验比对,论证了算法的可行性和有效性.展开更多
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.
文摘由于传统的煤层气产能分析算法存在影响因素不够全面,运行效率低和人为设置聚类参数缺乏说服力的问题。因此,该文在煤层气产能分类的基础上,对分类结果进行回溯,挖掘煤层气产能影响因素的规律,将基于密度聚类算法(Density-Based Spatial Clustering of Application with Noise,DBSCAN)与频繁模式增长算法(Frequent-Pattern Growth,FP-Growth)关联度分析算法优化结合,提出新的基于DBSCAN的FP-growth煤层气产能分析模型,找出影响煤层气产能的关键因素及其对应的参数范围。该文是深度学习与煤层气开发交叉学科的应用与研究,致力于煤层气产能分析评价体系的构建,为提高煤层气单井产气量,提升措施选井的决策效率有积极影响。
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.
文摘云计算为大数据提供了展示和共享的平台.为了防止隐私泄露,这些数据中往往包含人为添加的不确定因素,如何挖掘这些不确定数据是大数据共享亟待解决的问题.在用于共享的大数据中,不确定数据通过对精确数据的泛化处理来实现,具有均匀分布特性,这一特性不利于精确查询,但可为关联规则的挖掘提供便利条件.首先,依据泛化值之间可能的相交或包含关系,将泛化值进行分层聚类,为了保存与不确定数据集挖掘相关的重要信息,给出了构建不确定频繁模式树的算法,在此基础上,提出了频繁项集挖掘子算法(data mining algorithm for uncertain frequent item-sets,UFI-DM)和关联规则生成子算法(algorithm for generating association rules,GAR),分别用于挖掘频繁项集和生成关联规则,最后,通过理论分析和实验比对,论证了算法的可行性和有效性.
