通过对二维空间数据测试表明，改进的算法能够快速有效地挖掘出数据集中的离群数据，速度上数倍于原来的算法。
Experimental results show that the improved algorithm is effective and efficient in outlier mining and it is faster than the original algorithm.
离群数据挖掘是数据挖掘研究的一个重要分支。
Outlier data mining is an important embranchment in data mining research.
为了解决大规模数据中的异常检测问题，提出了基于支持向量数据描述（SVDD）的高效离群数据检测算法。
To efficiently resolve outlier detection problem in large scale data sets, an efficient outlier detection algorithm based on Support Vector Data Description (SVDD) was proposed.
通过对离群数据来源及特性进行分析，定义了离群贡献度的概念，提出了一种基于特征赋权的离群数据再聚类算法。
By analyzing the origin and feature of outliers, a concept of exceptional contribution degree is defined and then an algorithm for re-clustering outliers based on feature weighting is proposed.
针对传统数据抽查方法很难保证数据抽查有效性的缺点，结合离群数据挖掘，给出了一种基于离群数据挖掘的数据抽查新方法。
A new method of data spot checking based on outlier mining is proposed, which promises a solution to the lack of validity using traditional data spot checking method.
根据课题背景，给出一个针对时序数据的离群数据挖掘算法的改进算法。
Based on the project background, an improved outlier data mining algorithm for time series data is given out.
在宇宙中，寻求特殊的、未知的天体是人类探索宇宙奥妙所追求的目标之一，天体光谱离群数据识别方法是实现该目标的有效手段之一。
A recognition method of celestial spectra outliers based on concept lattice is proposed by regarding the intension of the concept lattice nodes as characteristic subspace of the celestial spectra.
针对分布式数据流环境，提出基于核密度估计的分布数据流离群点检测算法。
This paper presents an algorithm for outlier detection in distributed data streams.
接下来将了解InfoSphere Warehouse如何检测离群值，以及如何对数据应用偏差检测。
In the following, learn how InfoSphere Warehouse detects outliers and how you can apply deviation detection to your data.
原始点云经梯度方向迭代移动后，过滤噪音和剔除离群点，并修补点云缺失数据。
Points are moved onto the iso-surface by an iterative clustering along gradient field, where the noise and outliers are removed and defective data are repaired.
偏差检测是高度交互性的任务，通常需要手动检查离群值，以查明是否存在欺诈倾向、数据错误或者潜在的机遇。
Deviation detection is a highly interactive task, and outliers must usually be checked manually to see whether they indicate fraud, errors in the data, or some interesting opportunity.
实验表明，该算法能有效地识别离群点，同时能反映出数据对象在数据集中的孤立程度。
Experimental results show that: the algorithm can effectively identify outliers, at the same time, data objects reflect the isolation level in the data set.
传统的离群点挖掘算法无法有效挖掘数据流中的离群点。
The traditional algorithm of mining outliers cannot mine outliers in data stream effectively.
针对数据流的无限输入和动态变化等特点，提出一种新的基于距离的数据流离群点挖掘算法。
Concerning the infinite input and dynamic change in data stream environment, a new algorithm for detecting data stream outliers based on distance was proposed.
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。
According to the characteristics of spatial data sets, this paper proposes an outlier detection algorithm based on the Space Local Deviation Factor (SLDF).
在对时序数据进行离群检测之前，一般先将原时序数据划分为若干个子序列，以便降低计算复杂度。
General approaches for outlier detection need to divide temporal data into sub-sequences so as to reduce complexity.
离群点发现是数据挖掘的一项重要技术。
Outlier detection is a very important technique in data mining.
一种新的抽样方法是把数据挖掘技术中的分类、聚类及离群点挖掘等应用到审计风险管理中去。
It applies classification rule mining, clustering rule and outlier mining to the management of Audit Risk.
摘要：空间离群模式探测是空间数据挖掘的一个研究热点。
Abstract: spatial outlier detection is a research hotspot in the domain of spatial data mining.
首先，如果检查离群值的专家有限，那么可以使用具有最高偏差度的集群的数据记录。
First, if you only have a limited number of experts that are able to check outliers, you simply use the data records that belong to clusters with the highest deviation degree.
目前，离群挖掘正逐渐成为数据库、机器学习、统计学等领域研究人员的研究热点。
At present, outlier data mining is a hotspot for the researchers of database, machine learning and statistics.
一种新的抽样方法是把数据挖掘技术中的分类、聚类及离群点挖掘等应用到审计风险管理中去。
A new sampling method is proposed, which USES the latest technologies of database. It applies classification rule mining, clustering rule and outlier mining to the management of Audit Risk.
针对三维扫描仪获取的带噪声和离群点的点云数据，提出了基于局部离群点概念的去噪算法。
Concerning the point clouds with noises and outliers acquired by a 3d scanner, a denoising method based on the concept of local outlier was proposed.
实验结果表明，与最小二乘法相比，当数据中存在个别误差较大的离群点时，采用最小绝对偏差法可以使得定位精度得到明显改善。
Experimental results show that compared with least squares, least absolute deviation can improve location accuracy effectively considering the existence of several outliers.
接下来的小节将提供一个例子，以逐步演示如何用InfoSphereWarehouse发现离群值，以及如何为各个数据记录赋予偏差度。
The following section provides a step-by-step example of how to find outliers with InfoSphere Warehouse and how to assign deviation degrees to individual data records.
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。
The algorithm uses SLDF to measure the deviate degree of space points object. It calculates all the points' SLDF, sorts by their values, and uses the top M as the space outlier.
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。
The algorithm uses SLDF to measure the deviate degree of space points object. It calculates all the points' SLDF, sorts by their values, and uses the top M as the space outlier.
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