针对协方差交集的最优融合问题,提出了一种基于网格剖分的协方差交集融合新算法.该算法首先利用几何法对位置空间进行网格剖分,求出局部航迹协方差椭圆所包含的网格集合;其次,对所有的网格集合进行与运算,求出局部航迹协方差椭圆所包含的公共网格集合;最后,利用公共网格集合逼近协方差椭圆的交集,求出融合估计值.蒙特卡罗仿真试验证明,该方法能有效提高相关误差航迹的融合精度.

@@@@Aiming at the optimal fusion of the covariance intersection algorithm, a new covariance intersection algorithm based on the grid partition is presented for position fusion . Firstly, the position area is partitioned into many small rectangle grids by geometric method and the error ellipse of the local track is transformed into the set of the rectangle grids . Secondly, the common grids set are computed by logic and operation for the set of the rectangle grids . Finally, the optimal approximation for the covariance ellipse of the fusion estimation is computed by the common grids set and the estimated value of the fusion track is calculated by the common grids set . The Monte Carlo simulation shows the method can improve the precision of fusion track .

混合属性聚类是近年来的研究热点,对于混合属性数据的聚类算法要求处理好数值属性以及分类属性,而现存许多算法没有很好得平衡两种属性,以至于得不到令人满意的聚类结果.针对混合属性,在此提出一种基于交集的聚类融合算法,算法单独用基于相对密度的算法处理数值属性,基于信息熵的算法处理分类属性,然后通过基于交集的融合算法融合两个聚类成员,最终得到聚类结果.算法在UCI数据集Zoo上进行验证,与现存k-prototypes与EM算法进行了比较,在聚类的正确率上都优于k-prototypes与EM算法,还讨论了融合算法中交集元素比的取值对算法结果的影响.

Mixed attributes data clustering is a research hotspot in recent years. For mixed attributes data clustering algorithm, it requires handling numeric attributes and categorical attributes simultaneously. However many algorithms have not very good balance with numeric and categorical attributes, and the cluster results are not satisfied. For mixed attributes data set, a new clustering ensemble algorithm based on intersection is proposed. It processes the numeric attributes with a new relative density clustering algorithm, and processes the categorical attributes with a clustering algorithm based on information entropy. Then it fuses these two cluster members with a cluster fusion algorithm based on intersection. Finally, it gets the clustering results. It is validated by taking an experiment on UCI data set Zoo, and compared with the existing k-prototypes algorithm and EM algorithm. The experiment result shows that the new algorithm has higher flexibility and accuracy. The infl

本文提出了基于父节点的XML查询优化算法,它主要利用关键词的顺序循环查找父节点,把查找出来的父节点集合求交集,所得的父节点交集集合中的元素即为最小子树根节点,最后根据所得出的最小子树根节点得到用户所需的最紧致片段。通过选取经典的INEX数据集算法进行测试。实验结果表明,与传统的XML查询优化算法相比,基于父节点的XML查询优化算法有较高的准确率,更能满足用户需求。

This paper puts forward the XML query optimization algorithm based on father nodes, which circles to find father nodes by the order ofkeywords, and then gets the intersection of father nodes. The elements in the intersection of father nodes are smallest and lowest root nodes. Finally, it gets the most compact segment needed by users according to the smallest and lowest root nodes of different levels.By choosing the classic data set （INEX）, the algorithm is tested. Contrasting to traditional query methods, the experimental results have proved that the XML query optimization algorithm based on father nodes has higher accuracy result and meets the user＇s need better

基于Hadoop分布式计算平台,给出一种适用于大数据集的并行挖掘算法。该算法对非结构化的原始大数据集以及中间结果文件进行垂直划分以确保能够获得完整的频繁项集,将各个垂直分块数据分配给不同的Hadoop计算节点进行处理,以减少各个计算节点的存储数据,进而减少各个计算节点执行交集操作的次数,提高并行挖掘效率。实验结果表明,给出的并行挖掘算法解决了大数据集挖掘过程中产生的大量数据通信、中间数据以及执行大量交集操作的问题,算法高效、可扩展。

Based on Hadoop distributed computing framework,propose a parallel algorithm for mining the large dataset. The presented al-gorithm divides the original large non-structured dataset and large middle result files into several smaller-scale data blocks by vertical partitioning pattern in order to ensure the completeness of the frequent item set. The algorithm can reduce the size of the data to be stored in each computing node and decrease the execution times that each computing node calculates the intersection operations by distributing the data blocks to the computing nodes to parallel mining in Hadoop distributed computing environment,and it can improve the efficiency of parallel mining. The experimental results show that the presented parallel mining algorithm can solve the problem that the mining large dataset will generate large amount of data communication and large number of operations for calculating intersection,and it is efficient and scalable.

现有空间拓扑关系模型多针对同种类的空间对象进行处理,在实际应用中具有一定的局限性.本文在4-交集模型的基础上,通过扩展4-交集矩阵,对凹形区域和带单洞区域间的拓扑关系进行了表示,得到凹形区域和带单洞区域间161种拓扑关系,并给出前10种拓扑关系的示意图.提出算法,并通过程序验证161种拓扑关系均可实现.证明所获得的161种基本关系的完备性和互斥性,通过与相关工作的比较可知该表示模型比其它相关模型表达力更强.

@@@@Most of the spatial topological relation models are dealing with the same kind of spatial object, which are restricted in the practical applications at some degree. 4-intersection matrix model is extended to represent the topological relations between a concave region and a simple region with a hole. Thus, totally 161 topological relations are obtained, in which the illustrations of the first 10 relations are given. The algorithm is also presented to prove that the 161 topological relations are all feasible in the real world. The topological relations are proved to be exclusive and complete. Compared with other relative research work, the representation model is more expressive than other similar models.