A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data...A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions： 1） the concept of the top-k prob- abilistic prevalent co-locations based on a possible world model is defined; 2） a framework for discovering the top- k probabilistic prevalent co-locations is set up; 3） a matrix method is proposed to improve the computation of the preva- lence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for ex- act solutions; 4） a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5） an ap- proximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets.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
由于传统离群点检测方法未对离群点进行判定,从而导致出现了检测速度慢、检测误差大的问题,为此提出一种海量不确定数据集中离群点快速检测的方法。优先判定出不确定数据集中的离群点,利用点排序识别聚类结构(Ordering points to identi...由于传统离群点检测方法未对离群点进行判定,从而导致出现了检测速度慢、检测误差大的问题,为此提出一种海量不确定数据集中离群点快速检测的方法。优先判定出不确定数据集中的离群点,利用点排序识别聚类结构(Ordering points to identify the clustering structure)算法完成,确定待检测离群点所需参数,计算出离群点的离群属性,根据离群属性计算结果,引入邻域密度构建离群点快速检测模型,设定模型中离群点检测阈值,实现不确定数据集中离群点的快速检测。由仿真结果得出,与传统检测方法相比,提出的方法算法运行耗时降低了50%以上,离群点的判定准确度更高,漏检、误检率大大降低,实现了离群点精度高、速度快的检测,对数据挖掘与预处理有显著的实践意义。展开更多
文摘A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions： 1） the concept of the top-k prob- abilistic prevalent co-locations based on a possible world model is defined; 2） a framework for discovering the top- k probabilistic prevalent co-locations is set up; 3） a matrix method is proposed to improve the computation of the preva- lence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for ex- act solutions; 4） a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5） an ap- proximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets.
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.
文摘由于传统离群点检测方法未对离群点进行判定,从而导致出现了检测速度慢、检测误差大的问题,为此提出一种海量不确定数据集中离群点快速检测的方法。优先判定出不确定数据集中的离群点,利用点排序识别聚类结构(Ordering points to identify the clustering structure)算法完成,确定待检测离群点所需参数,计算出离群点的离群属性,根据离群属性计算结果,引入邻域密度构建离群点快速检测模型,设定模型中离群点检测阈值,实现不确定数据集中离群点的快速检测。由仿真结果得出,与传统检测方法相比,提出的方法算法运行耗时降低了50%以上,离群点的判定准确度更高,漏检、误检率大大降低,实现了离群点精度高、速度快的检测,对数据挖掘与预处理有显著的实践意义。
