分组多支持度关联规则研究

关联规则是数据挖掘的重要任务之一,传统关联规则算法只有一个最小支持度,假设项出现的频率大致相同,而在谮实际中并非如此,由此产生了多支持度关联规则问题.该问题针对每个项给定不同的支持度,而在实际应用中项可以划分成若干个组,每组有一个支持度.由此提出了分组多支持度关联规则问题,针对该问题给出了基于多支持度性质对项进行分组的方法.该方法可以降低2-项候选集的数目.在此基础上,进一步给出了相应的多支持度关联规则发现算法,并通过实验证明了算法的有效性.     

多色点集直线划分的复杂性及其近似算法

多色点集划分研究如何将含有不同颜色点的平面划分为各个区域,每个区域中只包含一种颜色的点。这是计算几何中的一种组合优化问题。但是现有的多边形划分方式性能较差。为此,提出用直线来划分平面。针对平面上多色点集的直线划分,将其离散化,证明其可以被非确定性图灵机在多项式时间内判定。并将Max2SAT问题在多项式时间内归约到组合优化问题,证明多色点集直线划分为NP难,从而证明其是NP完全的。利用最优化版本的特有性质,运用贪心方法构造出多项式时间的近似算法,并L归约到Setcover问题,以此证明算法的近似比为O( lgn)。     

基于矢量斜率的分段线性拟合*

介绍了用统计矢量斜率进行平面数据点分段线性拟合的算法.对于欲拟合的一系列平面数据点,一般是有先后顺序的.首先给出了矢量斜率的定义,然后计算每个平面数据点的统计矢量斜率,根据各点矢量斜率值接近的情况,将数据点分割成组,拟合各组数据形成线段,把各线段首尾连接起来就得到了平面数据点的分段线性拟合.定义的矢量斜率包含大小和方向两方面信息,主值区间为（－4～＋4）,它在主值区间的变化与角度在（－180°～180°）区间中的变化一一对应,且它们的关系曲线有很好的线性度.使用传统斜率进行分段线性拟合,存在斜率值与角度的关系曲线线性度差、斜率取值有时趋向无穷等问题,这些问题影响了拟合的精度,并限制了算法的使用范围.矢量斜率克服了上述问题,从而提供了拟合曲线的质量,且算法可适用于任意曲线.算法时间复杂度为线性.     

鲁棒的点云曲线骨骼提取算法

为解决复杂点云数据的曲线骨骼提取问题,提出了一种鲁棒的点云曲线骨骼提取算法。该方法首先通过区域分割将点云模型分成多个弱凸面集,减少噪声点对骨骼提取的影响,然后在每个弱凸面集中根据对称点信息提取候选骨骼点,对候选骨骼点进行压缩和平滑,并采用最优平面法对骨骼点进行重定位,最后利用区域分割信息将各区域的骨骼点连接得到最终的曲线骨骼。实验结果表明,该方法不仅能够处理完整和非完整的点云数据,而且能够正确提取包含复杂形状的点云骨骼。     

平面离散点集的边界搜索算法

进行有限元仿真首先要建立有限元网格模型。使用不含有任何拓扑信息的离散点集直接进行网格划分可以快速、精确地建立网格模型。使用铺路法进行网格剖分是从边界开始向内生成网格单元。该文提出一种使用搜索盒的搜索平面离散点集边界的算法。该方法将离散点分配到搜索盒中，遍历位于边界的搜索盒，将其中的点连接成边界点链表。该算法能正确地搜索包含有凹点、孔洞特征的离散点集的边界，具有较强的通用性。文中介绍了算法的基本思想，并给出算例。     

一种鲁棒的指纹细节特征点匹配方法

指纹细节特征点匹配是指纹识别过程的核心部分,鲁棒的细节特征点匹配方法需要克服指纹的旋转、变形和真实特征点丢失的情况。该文通过引入支持模型来进行细节特征点匹配,获得了较好的结果。在对支持模型理论进行简单分析之后,详细介绍了所提出的一个鲁棒的基于支持模型的细节特征点匹配算法。该方法通过融合多个种子松弛匹配的结果,来获取每个细节特征点的约束支持。并通过每个对应点不同的支持度得到一一对应的细节特征点匹配结果。最后给出两个指纹细节特征点集的相似性水平。该算法具有较强的鲁棒性和稳定性,能够很好地解决指纹细节特征点匹配过程中存在的旋转、变形和真实细节特征点丢失等情况。最后给出的实验结果验证了该算法的有效性。     

覆盖平面点集的最窄圆环

给出判断一个覆盖平面有限点集的圆环达到最窄的一组充分条件，同时指了对于一般的平面有限点集而言其中的一个条件是不可放弃的，这个结果对于解决所谓圆度问题不仅有理论价值，而且有实际实际意义。     

平面点集凸壳的快速算法

提出一种计算平面点集凸壳的快速算法。利用极值点划分出四个矩形,它们包含了所有凸壳顶点,通过对矩形中的点进行扫描,排除明显不是凸壳顶点的点,剩余的点构成一个简单多边形。再利用极点顺序法判断多边形顶点的凹凸性并删除所出现的凹顶点,最终得到一个凸多边形即为点集的凸壳。整个算法简洁明了,避免了乘法运算（除最坏情况外）,从而节省计算时间。     

多测度Value集相似度量

针对单一测度度量Vague集相似性上的缺陷,提出了两种Vague集的多测度相似度量.给出了一种Vague集的单形体几何表示方法,亦即将Vague集的真、假隶属度和未知度表达为划分单形体同一平面的3个三角形,进而提出了适合于度量Vague集相似性的面积测度,并结合距离测度和未知度测度构造了Vague集多测度相似度量,以在空间上体现出“点-线-面”多特征相似性度量格局.实例验证了多测度相似度量的有效性和优越性.     

基于PostGIS的线要素空间聚合方法

针对当前常规方法线空间流量数据聚合效率低的问题，提出基于PostGIS的流空间线要素聚合方法。通过离散化所有的线为点集，建立点集的空间索引，计算点集的唯一点集，对唯一点集上的每个点累加所有与该点重叠的属性值并进行更新，根据点的序列号和上一步更新的属性值，重新连接该条线上所有的点，在属性值变化处进行打断形成新的线要素。在此基础上，去除空间重叠的线要素和图形为空的线要素得到最后的线聚合结果。实验结果表明，该方法以矢量数据形式输出的线聚合结果正确，提取速度显著提升，有效地解决了大量矢量线数据融合时常规方法效率低下的问题。     

K Nearest Neighbor Equality: Giving equal chance to all existing classes

The nearest neighbor classification method assigns an unclassified point to the class of the nearest case of a set of previously classified points. This rule is independent of the underlying joint distribution of the sample points and their classifications. An extension to this approach is the k-NN method, in which the classification of the unclassified point is made by following a voting criteria within the k nearest points.The method we present here extends the k-NN idea, searching in each class for the k nearest points to the unclassified point, and classifying it in the class which minimizes the mean distance between the unclassified point and the k nearest points within each class. As all classes can take part in the final selection process, we have called the new approach k Nearest Neighbor Equality (k-NNE).Experimental results we obtained empirically show the suitability of the k-NNE algorithm, and its effectiveness suggests that it could be added to the current list of distance based classifiers.     

三维地质界面网格模型构建算法研究

三维地质模型主要包含地质构造模型和地质属性模型。提出一种局部映射-边界控制的曲面三角化网格模型构建算法,与映射法相比,减少三维空间点映射到二维平面的计算过程,避免因多点到一点的映射关系而生成错误的三角化网格模型。基于地质测量数据特点,原始地质数据经处理后采用点集合形式表示,基于点集数据构建三维三角化网格模型,模拟地质界面的展布形态,控制三角网格质量。采用两种网格边界控制方法,在有边界约束数据和无边界约束数据条件下均能自动更新地质界面三角化网格模型边界。基于断层点数据集测试并展示算法构建的三维三角化网格模型可视化效果,通过断层面三角化网格模型能够反映断层面之间空间位置关系。     

A novel plane extraction approach using supervised learning

This paper presents a novel approach for the classification of planar surfaces in an unorganized point clouds. A feature-based planner surface detection method is proposed which classifies a point cloud data into planar and non-planar points by learning a classification model from an example set of planes. The algorithm performs segmentation of the scene by applying a graph partitioning approach with improved representation of association among graph nodes. The planarity estimation of the points in a scene segment is then achieved by classifying input points as planar points which satisfy planarity constraint imposed by the learned model. The resultant planes have potential application in solving simultaneous localization and mapping problem for navigation of an unmanned-air vehicle. The proposed method is validated on real and synthetic scenes. The real data consist of five datasets recorded by capturing three-dimensional(3D) point clouds when a RGBD camera is moved in five different indoor scenes. A set of synthetic 3D scenes are constructed containing planar and non-planar structures. The synthetic data are contaminated with Gaussian and random structure noise. The results of the empirical evaluation on both the real and the simulated data suggest that the method provides a generalized solution for plane detection even in the presence of the noise and non-planar objects in the scene. Furthermore, a comparative study has been performed between multiple plane extraction methods.     

高功率密度发电机测试集优化方法研究

针对高功率发电机开展测试性设计分析对提升其维修保障性水平具有重要意义;通过对高功率密度发电机的故障模式开展分析,得到发电机的典型故障模式,针对各故障模式总结了发电机故障检测所需监测的物理参数;然后,通过构建多信号流模型和开展故障-测试相关性分析对发电机测点进行改进,提出了基于故障-测试相关矩阵(D矩阵)的测试集优化方法;该方法通过合理优化测点集可提升测点对故障模式的覆盖水平并减少冗余测点;最后,通过测试性预计对比了改进前后发电机的故障检测率和故障隔离率指标,验证了提出的测点改进方法提升高功率密度发电机测试性水平的有效性,为发电机的测试性改进设计提供技术支持。     

Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine

The fault diagnosis for hydroelectric generator unit (HGU) is significant to prevent dangerous accidents from occurring and to improve economic efficiency. The faults of HGU involve overlapping fault patterns which may denote a kind of faults in the early stage or a subset of samples that caused by multi-fault. But until now it has not been considered in the traditional classifier of fault diagnosis for HGU. In this paper, a novel classifier combined rough sets and support vector machine is proposed and applied in the fault diagnosis for HGU. Instead of classifying the patterns directly, the fault patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. Next, for the fault patterns lying in the overlapped region, the reliability they belong to a certain class is calculated. At last, the proposed method is successfully applied in analyzing an international standard data set, as well as diagnosing the vibrant faults of a HGU. The results show that the proposed classifier can more properly describe the complex map between the faults and their symptoms, and is suitable to fault diagnosis for HGU.     

不平衡数据集中的组合分类算法

为提高少数类的分类性能,对基于数据预处理的组合分类器算法进行了研究.利用Tomek links对数据集进行预处理;把新数据集里的多数类样本按照不平衡比拆分为多个子集,每个子集和少数类样本合并成新子集;用最小二乘支持向量机对每个新子集进行训练,把训练后的各个子分类器组合为一个分类系统,新的测试样本的类别将由这个分类系统投票表决.数据试验结果表明,该算法在多数类和少数类的分类性能方面,都优于最小二乘支持向量机过抽样方法和欠抽样方法.     

Generalized principal component analysis (GPCA)

This paper presents an algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points. We represent the subspaces with a set of homogeneous polynomials whose degree is the number of subspaces and whose derivatives at a data point give normal vectors to the subspace passing through the point. When the number of subspaces is known, we show that these polynomials can be estimated linearly from data; hence, subspace segmentation is reduced to classifying one point per subspace. We select these points optimally from the data set by minimizing certain distance function, thus dealing automatically with moderate noise in the data. A basis for the complement of each subspace is then recovered by applying standard PCA to the collection of derivatives (normal vectors). Extensions of GPCA that deal with data in a high-dimensional space and with an unknown number of subspaces are also presented. Our experiments on low-dimensional data show that GPCA outperforms existing algebraic algorithms based on polynomial factorization and provides a good initialization to iterative techniques such as k-subspaces and expectation maximization. We also present applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views.     

基于半监督的联合分类方法

提出一种基于半监督的联合分类方法.该方法在训练过程中,先构造一个基于类中心思想的简易分类器,通过设定有效阈值,从未标记数据中挑选区别度较大的数据加入到SVM的训练集中;在分类过程中,根据待分类点与分类面的相对位置,结合SVM和KNN算法,分两种情况来对其进行分类.实验结果表明,该方法既能在一定程度上克服监督学习算法手动标记大量训练集的困难,又能相应地提高分类准确率.     

平面点匹配的一点校准算法

点模式匹配是一项重要的视觉课题。对于一个平面点集,由平移和旋转并伴有一定噪声作用产生另一点集,提出一个基于一点校准的点模式快速匹配算法,并推广到带有属性点的匹配问题中。基于一点校准的点模式匹配算法,其计算复杂性为O(mn),其中m,n分别是两个点集所含点的个数,比基于两点距离近似相等的校准匹配算法,其计算复杂性为O(m2nl)(其中l为第二个点集中与第一个点集中任两个点的距离近似相等的平均个数),极大地减少了计算量。     

The relative neighbourhood graph is a part of every 30°-triangulation

We study sets of points in the two-dimensional Euclidean plane. The relative neighbourhood graph (RNG) of a point set is a straight line graph that connects two points from the point set if and only if there is no other point in the set that is closer to both points than they are to each other. A triangulation of a point set is a maximal set of nonintersecting line segments (called edges) with vertices in the point set. We introduce angular restrictions in the triangulations. Using the well-known method of exclusion regions, we show that the relative neighbourhood graph is a part of every triangulation all of the angles of which are greater than or equal to 30°.