欧氏几何 meaning in English
euclidean geometry
Examples
- This treatise has researched on the construction of structured ldpc codes , including eg , bibd , semi - random - rotation and an approach based on vector - matrix proposed by us and compared random construction and structured construction through theoretical analysis and simulation . we also compared several structured codes
本论文研究了结构化ldpc码的构造,包括基于欧氏几何空间的eg ( euclideangeometries )方法、基于组合代数的bibd ( balancedincompleteblockdesign )方法、半随机半结构化的-旋转构造法,以及我们提出的基于矢量矩阵的结构化构造方法。 - By taking advantage of parallel lines and orthogonal lines in architecture , the camera internal parameters , rotation and translation can be recovered from a set of un - calibrated images via computing absolute conic and vanishing points . the euclidean 3d model of architecture ( up to a scale factor ) can be recovered too
利用建筑物中常见的平行直线和正交直线等特点，通过绝对二次曲线和消影点等射影几何量的计算，可以从图象中恢复摄像机的内参数、旋转和平移位置，同时恢复建筑物的三维欧氏几何模型（相差一个尺度因子） 。 - This paper introduces the development of data mining and the concepts and techniques about clustering will be discussed , and also mainly discusses the algorithm of cluster based on grid - density , then the algorithm will be applied to the system of insurance ? among the various algorithms of cluster put forward , they are usually based on the concepts of distance cluster o whether it is in the sense of traditional eculid distance such as " k - means " or others o these algorithms are usually inefficient when dealing with large data sets and data sets of high dimension and different kinds of attribute o further more , the number of clusters they can find usually depends on users " input 0 but this task is often a very tough one for the user0 at the same time , different inputs will have great effect on the veracity of the cluster ' s result 0 in this paper the algorithm of cluster based on grid - density will be discussed o it gives up the concepts of distance < , it can automatically find out all clusters in that subspaceo at the same time , it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases o
在以往提出的聚类算法中，一般都是基于“距离( distance ) ”聚类的概念。无论是传统的欧氏几何距离( k - means )算法，还是其它意义上的距离算法，这类算法的缺点在于处理大数据集、高维数据集和不同类型属性时往往不能奏效，而且，发现的聚类个数常常依赖于用户指定的参数，但是，这往往对用户来说是很难的，同时，不同参数往往会影响聚类结果的准确性。在本文里要讨论的基于网格密度的聚类算法，它抛弃了距离的概念，它的优点在于能够自动发现存在聚类的最高维子空间；同时具有很好的处理高维数据和大数据集的数据表格的能力。