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1.
On Detecting Spatial Outliers 总被引:1,自引:1,他引:0
The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from
them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial
attribute values are significantly different from the values of their spatial neighbors. These objects, called spatial outliers,
may reveal important phenomena in a number of applications including traffic control, satellite image analysis, weather forecast,
and medical diagnosis. Most of the existing spatial outlier detection algorithms mainly focus on identifying single attribute
outliers and could potentially misclassify normal objects as outliers when their neighborhoods contain real spatial outliers
with very large or small attribute values. In addition, many spatial applications contain multiple non-spatial attributes
which should be processed altogether to identify outliers. To address these two issues, we formulate the spatial outlier detection
problem in a general way, design two robust detection algorithms, one for single attribute and the other for multiple attributes,
and analyze their computational complexities. Experiments were conducted on a real-world data set, West Nile virus data, to
validate the effectiveness of the proposed algorithms.
相似文献
Feng Chen (Corresponding author)Email: |
2.
局部离群点挖掘算法研究 总被引:14,自引:0,他引:14
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. 相似文献
3.
基于距离的孤立点检测研究 总被引:15,自引:0,他引:15
孤立点检测是一个重要的知识发现任务,在分析基于距离的孤立点及其检测算法的基础上,文章提出了一个判定孤立点的新定义,并设计了基于抽样的近似检测算法,用实际数据进行了实验。实验结果表明,新的定义不仅与DB(p,d)孤立点定义有着相同的结果,而且简化了孤立点检测对用户的要求,同时给出了数据对象在数据集中的孤立程度。 相似文献
4.
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。 相似文献
5.
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。 相似文献
6.
离群点是与其他正常点属性不同的一类对象,其检测技术在各行业上均有维护数据纯度、保障业内安全等重要应用,现有算法大多是基于距离、密度等传统方法判断检测离群点.本算法给每个对象分配一个"孤立度",即该点相对其邻点的孤立程度,通过排序进行判定,比传统算法效率更高.在AP(affinity propagation)聚类算法的基础上进行改进与优化,提出能检测异常数据点的算法APO(outlier detection algorithm based on affinity propagation).通过加入孤立度模块并计算处理样本点的孤立信息,并引入放大因子,使其与正常点之间的差异更明显,通过增大算法对离群点的敏感性,提高算法的准确性.分别在模拟数据集和真实数据集上进行对比实验,结果表明:该算法与AP算法相比,对离群点的敏感性更加强烈,且本算法检测离群点的同时也能聚类,是其他检测算法所不具备的. 相似文献
7.
香农的信息熵被广泛用于粗糙集.利用粗糙集中的粗糙熵来检测离群点,提出一种基于粗糙熵的离群点检测方法,并应用于无监督入侵检测.首先,基于粗糙熵提出一种新的离群点定义,并设计出相应的离群点检测算法-–基于粗糙熵的离群点检测(rough entropy-based outlier detection,REOD);其次,通过将入侵行为看作是离群点,将REOD应用于入侵检测中,从而得到一种新的无监督入侵检测方法.通过多个数据集上的实验表明,REOD具有良好的离群点检测性能.另外,相对于现有的入侵检测方法,REOD具有较高的入侵检测率和较低的误报率,特别是其计算开销较小,适合于在海量高维的数据中检测入侵. 相似文献
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In many domains, important events are not represented as the common scenario, but as deviations from the rule. The importance and impact associated with these particular, outnumbered, deviant, and sometimes even previously unseen events is directly related to the application domain (e.g., breast cancer detection, satellite image classification, etc.). The detection of these rare events or outliers has recently been gaining popularity as evidenced by the wide variety of algorithms currently available. These algorithms are based on different assumptions about what constitutes an outlier, a characteristic pointing toward their integration in an ensemble to improve their individual detection rate. However, there are two factors that limit the use of current ensemble outlier detection approaches: first, in most cases, outliers are not detectable in full dimensionality, but instead are located in specific subspaces of data; and second, despite the expected improvement on detection rate achieved using an ensemble of detectors, the computational efficiency of the ensemble will increase linearly as the number of components increases. In this article, we propose an ensemble approach that identifies outliers based on different subsets of features and subsamples of data, providing more robust results while improving the computational efficiency of similar ensemble outlier detection approaches. 相似文献
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基于距离的孤立点检测及其应用 总被引:13,自引:2,他引:13
孤立点检测是一个有趣的知识发现任务,文章介绍了基于距离的孤立点检测及其相关概念,分析了几种有代表性的算法。最后,文章给出了一个判定孤立点的新的定义,并按此定义进行了检测算法,用实际数据进行了实验。实验结果表明,新的定义不仅与DB(p,d)孤立点定义有着相同的结果,而且简化了孤立点检测对用户的需求,同时给出了数据对象在数据集中的孤立程度。 相似文献
12.
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。 相似文献
13.
Detecting and tracking regional outliers in meteorological data 总被引:1,自引:0,他引:1
Chang-Tien Lu 《Information Sciences》2007,177(7):1609-1632
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. 相似文献
14.
Automatic summary of databases is an important tool in strategic decision-making. This paper presents the application of linguistic summaries to outlier detection in databases containing both text and numeric attributes. The proposed method applies Yager’s standard summary based on interval-valued fuzzy sets. Fuzzy similarity measures are the features which are looked for. Detection of outliers can identify defects, remove impurities from the data, and, most of all, it may provide the basis for decision-making processes. In this paper, we introduce a definition of an outlier based on linguistic summaries. Feasibility of the method is demonstrated on practical examples. 相似文献
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一种两阶段异常检测方法 总被引:4,自引:0,他引:4
提出了一种新的距离和对象异常因子的定义,在此基础上提出了一种两阶段异常检测方法TOD,第一阶段利用一种新的聚类算法对数据进行聚类,第二阶段利用对象的异常因子检测异常.TOD的时间复杂度与数据集大小成线性关系,与属性个数成近似线性关系,算法具有好的扩展性,适合于大规模数据集.理论分析和实验结果表明TOD具有稳健性和实用性. 相似文献
17.
针对传统离群点检测算法在类极度不平衡的高维数据集中难以学习离群点的分布模式,导致检测率低的问题,提出了一种生成对抗网络(generative adversarial network,GAN)与变分自编码器(variational auto-encoder,VAE)结合的GAN-VAE算法。算法首先将离群点输入VAE训练,学习离群点的分布模式;然后将VAE与GAN结合训练,生成更多潜在离群点,同时学习正常点与离群点的分类边界;最后将测试数据输入训练后的GAN-VAE,根据正常点与离群点相对密度的差异性计算每个对象的离群值,将离群值高的对象判定为离群点。在四个真实数据集上与六个离群点检测算法进行对比实验,结果表明GAN-VAE在AUC、准确率和F;值上平均提高了5.64%、5.99%和13.30%,证明GAN-VAE算法是有效可行的。 相似文献
18.
对于离群点的形成,不同的属性起着不同的作用,离群点在不同的属性域中,会表现出不同的离群特性,在大多数情况下,高维数据空间中的对象是否离群往往取决于这些对象在低维空间中的投影。针对如何将离群点按照形成原因分类的问题,引入离群属性和离群簇等概念,以现有离群挖掘技术为基础,提出了基于离群分类来进行离群点分析的方法,实现了基于聚类的离群点分类算法CBOC(cluster-based outlier classification),以揭示离群点的内涵知识。实验表明了该方法在实际应用中的有效性。 相似文献
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数据挖掘以发现常规模式为主体,但离群数据在欺诈分析及安全领域具有重要分析价值,离群数据检测已成为数据挖掘的重要内容。对聚类与分类以及关联规则分析中典型的常规数据挖掘算法如何处理离群数据进行全面分析与总结,讨论了BIRCH、CURE、Chameleon、DBSCAN以及基于共享最近邻的聚类算法以及基于不平衡分类和基于非频繁模式的离群检测技术,给出了一种利用K-最近邻算法的离群数据检测方法,并报告了测试结果。 相似文献