孤立点
- 网络outlier;Isolated;isolated point;isolated vertex
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基于ICA与SVM的孤立点挖掘模型
Outlier Mining Model Based on ICA & SVM
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本文针对SVM的模型、核函数的构造、SVM参数选择和孤立点检测四个方面进行了研究。
This paper does the researches on four areas : SVM model , kernel function constructing , SVM parameter selection and outlier detection .
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设G是一个没有孤立点的简单图。
Let G be a simple graph with no isolated vertices .
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基于孤立点和初始质心选择的k均值算法的改进与应用
Application of an improved k-means algorithm based on outliers and original clustering center
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另外,本文的XML孤立点数据清理算法也能达到较高的准确率和查全率。
In addition , outlier data cleaning method for XML can achieve higher accuracy and the recall level .
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首先,对kmeans算法中孤立点检测问题进行深入研究,提出了基于网格的数据预处理算法。
Firstly , after outlier detection problem of the k_means algorithm is studied deeply , a grid-based data pre-processing algorithm is proposed .
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基于孤立点检测的RFID数据流清洗技术研究
Research on Cleaning Techniques over RFID Data Stream Based on Outlier Detection Methods
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基于距离的分布式RFID数据流孤立点检测
Distant-based Outlier Detection for Distributed RFID Data Streams
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基于统计聚类RBF神经网络的孤立点检测研究
A New Isolated Point Detecting Algorithm Based on Statistical Clustering RBF Neural Network
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设G是有n个点的图,G的一个匹配是指G的一个生成子图,它的每个分支或是孤立点或是孤立边。
One match of G is a generating subgraph of G. Every branch is a single point or a single side .
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基于PCA及属性距离和的孤立点检测算法
Algorithm for outlier detection based on principal component analysis and sum of attributes distance
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基于改进微粒群算法的K-MEANS聚类和孤立点查找
K-Means Clustering and Outlier Detection Based on PSO
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针对经典K–means算法易受噪声和孤立点影响这一缺点,对算法做了进一步改进,以减少噪声和孤立点对聚类效果的影响。
Point to classical K-means algorithm vulnerable to noise and the impact of isolated point defects I have improve the algorithm to reduce the noise and isolated points on the cluster effect .
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K均值算法的聚类个数K需指定,聚类结果与数据输入顺序相关,而且易受孤立点影响。
K-means algorithm has some deficiencies . The number K must be pointed and its effectiveness liable to be effected by isolated data and the input sequence of data .
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基于CD-Tree与SOD,设计了新的孤立点检测算法。
Finally , the CD-Tree-based algorithm is designed for outlier detection based on CD-Tree and SOD .
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比如初始聚类数K要事先指定,初始聚类中心选择存在随机性,算法容易生成局部最优解,受孤立点的影响很大等。
For example we must choose the initial clustering number . The choice of initial clustering centre has randomness . The algorithm receives locally optimal solution easily , the effect of isolated point is serious .
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在此基础上提出一种改进的K-Means算法,主要是改进原来的算法对孤立点比较敏感的缺点。
Based on it , an improved algorithm of K-Means is proposed , it can conquer disadvantage that customary algorithm is effected by the isolated point .
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然而单纯的Hausdorff距离对噪声和孤立点均比较敏感,导致误匹配率较高。
Hausdorff distance , however simple on the noise and isolated points are more sensitive , resulting in a higher rate of false matches .
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限制边割将连通图分离成不含孤立点的不连通图,如果最小限制边割只能分离孤立边,则称图G是超级限制边连通的。
Restricted edge cut separates a connected graph into a disconnected one without isolated vertex . Graph G is super restricted edge connected if no subgraph but an isolated edge can be separated by any minimum restricted edge cut .
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该方法在去噪前,先用定位精度高的小尺度LOG算子检测图像的边缘,对检测出的边缘进行均值平滑滤波,以减少边缘图像中的孤立点噪声;
Before denoising , the edge of a noised image was detected with small-scale LOG operator which had higher orientation precision , and the image edge was smoothed with average filter to reduce a great lot of isolated point .
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此外,我们还将提出的算法与现有的一些算法在KDD常用数据集上进行实验比较,并得到了更好的孤立点的去除性能。
Furthermore , we also compared our algorithm against some existing methods on the top of the KDD dataset and got better outlier removal performance .
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介绍了文档聚类中基于划分的k-means算法,k-means算法适合于海量文档集的处理,但它对孤立点很敏感。
This paper first introduces the partitioning-based k-means algorithms for documents clustering . The k-means algorithm adapts to processing the vast amount of documents , but it is sensitive to outliers .
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为了克服K-Means方法对孤立点敏感性的缺点,并进一步提高聚类的质量和时间效率,本文将基于密度的聚类算法应用于文本对象之上。
To eliminate the sensitivity to outliers in K-Means and to improve the clustering efficiency and performance further more , density-based clustering algorithm is applied to document clustering in this thesis .
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其次在对聚类算法进行研究总结的基础上,提出了将粒子群优化算法和k-means算法相结合的PSO+孤立点+k-means的新型聚类算法。
Second the subject of particle swarm optimization is put forward to work within the clustering algorithm and k-means algorithm combined together , which is efficient and convenient in multidimensional clustering method .
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考虑极小极大定理中所获临界点集是否含有鞍点的问题,在不假设临界值为R1中孤立点的情况下获得了鞍点的存在性定理。
The existence of the saddle points generated by Mini-max theorem is studied under the case that the critical value need not be isolated point in R.
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为了消除普通FCM算法中随机初始化和孤立点对算法聚类效果的影响,本文提出了改进FCM算法。
In order to eliminate the effect of the random initialization and isolated point on the clustering result in the fuzzy C means ( FCM ) algorithm , an improved FCM algorithm is presented .
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该算法是对基于局部稀疏系数(LSC)孤立点挖掘论文中局部稀疏率和局部稀疏系数计算的一种改进。
This algorithm is an improvement of local sparsity ratio and local sparsity coefficient computation for Local Sparsity Coefficient-Based ( LSC ) Mining of Outliers paper .
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有限混合(FM)模型已经广泛地应用于图像分割,但是由于没有考虑空间信息,导致分割的结果对噪声很敏感,分割出的区域存在很多杂散的孤立点。
The conventional finite mixture model ( FM ), being widely used in image segmentation , does not take the spatial information into account , which leads this model to work only on well defined images .
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再考虑到现实中的数据质量问题,鉴于树算法对孤立点有免疫力和自动处理缺失数据的优点,所以选择CART树算法作为主要建模工具。
When data quality issue in reality is also taken into account , we choose CART as the modeling tool , given that tree algorithm is immune to outliers and can deal with missing values automatically .
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相对传统的k-means算法,本文算法不仅具有能有效地处理孤立点,有较好的抗噪声能力,而且不需要设置簇(聚类中心)数目的特点。
Contrast to the traditional k-means algorithm , the algorithm has the features of dealing with the isolated dot effectively , having better anti-noise capability , and not needing to set the number of cluster .