空间离群点的检测算法

空间离群点是指与其邻居具有明显区别的属性值的空间对象。已有的空间离散点检测算法一个主要的缺陷就是这些方法导致一些真正的离群点被忽略而把一些非离群点当成了空间离群点。提出了一种迭代算法,该算法通过多次迭代检测离群点,取得较好效果。实验表明该算法具有较好的实用性。     

空间离群点的检测算法

空间离群点是指与其邻居具有明显区别的属性值的空间对象.已有的空间离散点检测算法一个主要的缺陷就是这些方法导致一些真正的离群点被忽略而把一些非离群点当成了空问离群点.提出了一种迭代算法,该算法通过多次迭代检测离群点,取得较好效果.实验表明该算法具有较好的实用性.     

基于Voronoi和空间自相关的离群点检测

为了提高空间数据挖掘的效率和准确度,在分析传统的离群点检测算法优、缺点的基础上,提出一种空间离群点检测算法。用Voronoi来确定空间对象间的邻近关系,在空间邻域内利用空间自相关性来计算局部Moran指数,并将其作为离群因子进而判断离群点。实验结果表明,该算法能够高效、准确地检测出空间离群点,具有对用户依赖性少和可伸缩性强等优点。     

基于Voronoi和空间自相关的离群点检测

为了提高空间数据挖掘的效率和准确度,在分析传统的离群点检测算法优、缺点的基础上,提出一种空间离群点检测算法。用Voronoi来确定空间对象间的邻近关系,在空间邻域内利用空间自相关性来计算局部Moran指数,并将其作为离群因子进而判断离群点。实验结果表明,该算法能够高效、准确地检测出空间离群点,具有对用户依赖性少和可伸缩性强等优点。     

基于Voronoi和信息熵的空间离群样点检测

为了提高离群点挖掘的效率和准确度,在分析了传统离群点挖掘算法优、缺点的基础上,提出一种离群点检测算法.该算法利用Voronoi确定样点之间的邻近关系,通过参照邻域范围内其它样点的非空间属性值的信息熵作为离群因子,并根据离群因子标识出样点集中的离群点.以北京市大兴区土壤养分为例,实验结果表明,该检测算法能够高效,准确地检测出土壤样点中的离群点.     

基于空间局部偏离因子的离群点检测算法

针对空间数据集的特性,提出一种基于空间局部偏离因子(SLDF)的离群点检测算法。利用SLDF度量空间点对象的离群程度,计算空间数据集中点对象的SLDF值并对其进行排序,将取值较大的前M个点对象作为空间离群点。实验结果表明,该算法能较好地检测空间局部离群点,其有效性与准确性均优于SLZ算法,适用于高维大数据集的空间离群点检测。     

基于空间约束的离群点挖掘

由于现有的空间离群点检测算法没有很好地解决空间数据的自相关性和异质性约束问题,提出用计算邻域距离的方法解决空间自相关性约束问题,用计算空间局部离群系数的方法解决空间异质性约束问题。用离群系数表示对象的离群程度,并将离群系数按降序排列,取离群系数最大的前m个对象为离群点,据此提出基于空间约束的离群点挖掘算法。实验结果表明,所提算法比已有算法具有更高的检测精度、更低的用户依赖性和更高的效率。     

基于全息熵的空间离群点挖掘算法研究

基于距离和基于密度的离群点检测算法受到维度和数据量伸缩性的挑战, 而空间数据的自相关性和异质性决定了以属性相互独立和分类属性的基于信息理论的离群点检测算法也难以适应空间离群点检测, 因此提出了基于全息熵的混合属性空间离群点检测算法。算法利用区域标志属性进行区域划分, 在区域内利用空间关系确定空间邻域, 并用R*-树进行检索。在此基础上提出了基于全息熵的空间离群度的度量方法和空间离群点挖掘算法, 有效解决了混合属性的离群度的度量和离群点的挖掘问题。由于实现区域划分有利于并行计算, 从而可适应大数据量的计算。理论和实验证明, 所提算法在计算效率和实验结果的可解释性方面均具有优势。     

基于全局最近邻的离群点检测算法

针对全局最近邻离群点检测算法的效率问题,为了能够在数据集中快速准确地检测离群点,运用属性约简技术,将离群点的搜索简约到较小的最具代表性的属性子空间中进行,从而有效降低属性空间搜索的复杂度。在此基础上,通过计算基于近邻的加权离群因子实现离群点的检测并提出了相应的算法。实验表明,该离群点算法具有较好的适应性和有效性。     

局部离群点挖掘算法研究

离群点可分为全局离群点和局部离群点.在很多情况下,局部离群点的挖掘比全局离群点的挖掘更有意义.现有的基于局部离群度的离群点挖掘算法存在检测精度依赖于用户给定的参数、计算复杂度高等局限.文中提出将对象属性分为固有属性和环境属性,用环境属性确定对象邻域、固有属性计算离群度的方法克服上述局限;并以空间数据为例,将空间属性与非空间属性分开,用空间属性确定空间邻域,用非空间属性计算空间离群度,设计了空间离群点挖掘算法.实验结果表明,所提算法具有对用户依赖性少、检测精度高、可伸缩性强和运算效率高的优点.     

多代表点特征树与空间聚类算法

空间数据具有海量、复杂、连续、空间自相关、存在缺损与误差等的特点，要求空间聚类算法具有高效率，能处理各种复杂形状的簇，聚类结果与数据空间分布顺序无关，并且对离群点是健壮的等性能，已有的算法难以同时满足要求。本文提出了一个适合处理海量复杂空间数据的数据结构一多代表点特征树。基于多代表点特征树提出了适合挖掘海量复杂空间数据聚类算法CAMFT，该算法利用多代表点特征树对海量的数据进行压缩，结合随机采样的方法进一步增强算法处理海量数据的能力；同时，多代表点特征树能够保存复杂形状的聚类特征，适合处理复杂空间数据。实验表明了算法CAMFT能够快速处理带有离群点的复杂形状聚类的空间数据，结果与对象空间分布顺序无关，并且效率优于已有的同类聚类算法BLRCH与CURE。     

基于Delaunay三角网的空间离群挖掘

空间离群是指非空间属性与其空间邻居显著不同的空间对象。空间数据的特殊性决定了空间离群挖掘需要充分考虑空间数据的特点,才能挖掘出有现实意义的离群。本文对现有主要的空间数据离群挖掘算法进行了研究分析,针对k-邻域法确定空间邻域的缺点,基于Delaunay三角网在表达空间邻近关系的有效性,通过构建Delaunay三角网确定空间邻域并生成空间权重矩阵,据此提出了基于Delaunay三角网的空间离群挖掘算法DT_SOF,并以实际生态地球化学数据进行实验检验。结果表明,算法具有较低的用户依赖性,能准确挖掘空间离群。     

空间孤立点检测

空间孤立点是指与邻居具有不连续性的空间点,或者是偏离观测值以至使人们认为是由不同的体系产生的。空间孤立点检测在交通、生态、公共安全、卫生健康、地震、海啸等领域有广泛应用。传统的根据一个非空间属性值进行孤立点判断的方法客易引起孤立点判断失误。作者在针对多个属性进行考虑的基础上,提出以空间维确定邻居关系,非空间维定义距离函数,使用Mahalanobis距离检测孤立点,研究一种新的检测空间孤立点的算法。并时时间复杂度进行分析。仿真实验说明算法可以有效地发现大规模空间数据中的孤立点。     

Adaptive spatial information-theoretic clustering for image segmentation

The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants.     

含局部空间约束的t分布混合模型的点集配准

基于高斯混合模型（Gaussian mixture model,GMM）的点集非刚性配准算法易受重尾点和异常点影响,提出含局部空间约束的t分布混合模型的点集非刚性配准算法. 通过期望最大化（Expectation maximization,EM）框架将高斯混合模型推广为t分布混合模型;把Dirichlet分布作为浮动点的先验权重,并构造含局部空间约束性质的Dirichlet 分布参数. 使用EM算法获得配准参数的闭合解;计算浮动点的自由度,改变其概率密度分布,避免异常点水平估计误差. 实验表明,本文提出的配准算法具有配准误差小、鲁棒性好、抗干扰能力强等优点.     

Cross-validation as a means of investigating DEM interpolation error

Studies of the detailed characteristics of DEM error have been hampered by the difficulty in obtaining a large sample of error values for a DEM. The approach proposed in this paper is to resample a DEM to a lower resolution and then reinterpolate back to the original resolution which produces a large sample of error values well distributed across the DEM. This method is applied to a sample area from Scotland, which contains a variety of terrain types. The results show that the standard measure of error, the root mean square error (RMSE) of elevation, shows only moderate correlation with a visual assessment of the quality of DEMs produced by a range of interpolation methods. The frequency distribution and strength of spatial autocorrelation are shown to vary with the initial data density and interpolation method. When the source data density is low, the error has strong spatial autocorrelation and a distribution that is close to being Gaussian. However, as the data density increases, levels of spatial autocorrelation drop and the distribution becomes leptokurtic with values very strongly clustered around zero. At the level of the individual DEM point, elevation error is shown to be a poor predictor of error in slope derivatives which depend on the spatial pattern of elevation errors around the point and are also sensitive to differences in terrain. At the level of a whole DEM, however, RMSE of elevation is a good predictor of RMSE in gradient and aspect but not of curvature.     

Detecting and tracking regional outliers in meteorological data

Detecting spatial outliers can help identify significant anomalies in spatial data sequences. In the field of meteorological data processing, spatial outliers are frequently associated with natural disasters such as tornadoes and hurricanes. Previous studies on spatial outliers mainly focused on identifying single location points over a static data frame. In this paper, we propose and implement a systematic methodology to detect and track regional outliers in a sequence of meteorological data frames. First, a wavelet transformation such as the Mexican Hat or Morlet is used to filter noise and enhance the data variation. Second, an image segmentation method, λ-connected segmentation, is employed to identify the outlier regions. Finally, a regression technique is applied to track the center movement of the outlying regions for consecutive frames. In addition, we conducted experimental evaluations using real-world meteorological data and events such as Hurricane Isabel to demonstrate the effectiveness of our proposed approach.     

A new image segmentation algorithm with applications to image inpainting

This article describes a new approach to perform image segmentation. First an image is locally modeled using a spatial autoregressive model for the image intensity. Then the residual autoregressive image is computed. This resulting image possesses interesting texture features. The borders and edges are highlighted, suggesting that our algorithm can be used for border detection. Experimental results with real images are provided to verify how the algorithm works in practice. A robust version of our algorithm is also discussed, to be used when the original image is contaminated with additive outliers. A novel application in the context of image inpainting is also offered.