孤立点分析是数据挖掘中的一个重要课题。
Analysis of outlier mining is one of the important problems in data mining.
对孤立点的识别就是对数据集小模式的研究。
Identifying outliers from data set aims at researching on the small pattern of data set.
本文主要研究了基于孤立点因子的增量式挖掘技术。
We study the incremental data mining technology based outlier factor.
基于偏离的孤立点探测方法是孤立点分析一个重要的技术。
The offset-based outlier mining detecting method is a key technique for analysis of outlier mining.
提出了一种基于局部孤立系数(loc)的孤立点挖掘算法。
This paper presents a Local Outlier Coefficient - Based (LOC) Mining of Outliers.
目前，在时间序列分析领域，孤立点的挖掘越来越多的受到重视。
At present, outlier mining has attached a great importance in the field of time series analysis.
在多目标决策和综合评价中，有个别对象远远偏离群体，成为孤立点集。
There are some objects that deviate the mass in the multiple objectives decision making (MODM) and comprehensive evaluating. So they are called outlier set.
SVG将M位置定义为一个孤立点的位置，之前没有任何与这个点相连的点。
SVG defines that M position to the location where a point shall be placed without connection to any previous point.
实际目标不一定存在孤立点，本文提出了一种对连片目标回波运动补偿的新方法。
However, in reality, this isolated point can not always be found. A new scheme which can compensate the motion effects for continuous target return is presented in the paper.
它不仅对样本的输入顺序敏感，可能产生局部最优解，而且受孤立点的影响很大。
Not only is it sensitive to the order of sample data, but also it may make out the local excellent and be affected by the outliers.
该文提出了一个基于相似系数和检测孤立点的聚类算法，有效地解决了这个问题。
This article puts forward a clustering algorithm toc heck outlier based on similar coefficient sum and it efficiently solves this problem.
该算法使用了基于距离的技术来处理孤立点，引进了一种基于扩展的方法进行聚类。
The algorithm deals with outliers by the technique of distance-based and clusters by the method of extension-based.
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.
相互交织的楼梯和通道意味着你可以从一个孤立点的尽头到达另一个，却从不接触地面。
An interwoven series of stairs and passageways meant you could travel from one end of the enclave to the other, without ever touching ground.
提出了一种基于密度的孤立点因子算法和一种基于粗集理论的属性类别差异数据归约算法。
The Out-lier algorithm based on density and attribution classical discrepant data protocol algorithm based on rough set theory were presented.
这种方法使用了基于密度的孤立点挖掘的主要思想，用克隆选择算法进行数据立方体搜索。
It is a dense-based method and the low-dense data cubes are searched by clonal selection algorithm.
孤立点挖掘又称孤立点分析、异常检测、例外挖掘、小事件检测、挖掘极小类、偏差检测。
The problem of outlier mining has been variously called outlier analysis, anomaly detection, exception mining, detecting rare events, mining rare classes, deviation detection, etc.
判断检测出的孤立点是否可疑；通过对可疑孤立点进行审计专业判断，从而发现审计线索。
The doubtful outliers are validated by using professional audit method so as to find the clues.
运动补偿是ISAR成像的关键问题之一，通常都是利用目标的一个孤立点作基准进行补偿。
Motion compensation is one of the key problems in ISAR imaging. Usually, the compensation process is based on an isolated point on target.
局部切空间排列(LTSA)算法是一种有效的流形学习方法，但该算法对孤立点的存在非常敏感。
As an effective manifold-learning method, the local tangent space alignment (LTSA) algorithm is sensitive to outliers.
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.
通过概念聚类识别孤立点，运用规划识别技术和贝叶斯因果网络实现目标的预测、识别，最终实现系统自学习。
The system applies conceptual clustering technology to recognize outliers, and uses plan recognition and causal network to predict and recognize the target.
支持向量机(SVM)是解决回归问题的一种有效的方法，但传统的支持向量机对样本中的噪声和孤立点非常敏感。
Support vector machine (SVM) is an effective method for resolving regression problem, however, traditional SVM is very sensitive to noises and outliers in the training sample.
给出了两公式间的DL3 -相似度与伪距离的概念，并建立了DL3 -逻辑度量空间，证明了此空间没有孤立点。
The conceptions of DL3-similarity degree and pseudo-metric on two formulas are given. DL3-logic metric space is built. And it is proved that this space has no isolated point.
为解决单个帖子线索的多话题性问题，识别聚类中的孤立点，提出一种基于模糊聚类的网络论坛（BBS）热点话题挖掘算法。
A bulletin board system(BBS) hot topic mining algorithm based on fuzzy clustering was developed to solve the problem of the post thread with multiple topics and identifying the outliers in clustering.
在模式挖掘方面，集成了目前有效的最大向前路径挖掘算法和频繁遍历路径挖掘算法，并且将孤立点分析方法引入日志挖掘中。
Efficient mining algorithm of maximum forward path and efficient mining algorithm of frequent traversal path are integrated in the mining period; Outlier analysis is introduced into the mining system.
为此，把孤立点分析算法应用于设备维护案例不一致性的辨识，并对一组实际的风机机组案例库片断进行孤立点分析，找出了不一致案例。
So, a technology of plant maintenance case inconsistency identification on Outlier analysis is provided, and in this way, an inconsistency case is found out from a set of blower fan team's case bases.
基于网格的多密度聚类算法不仅能够对数据集进行正确的聚类，同时还能有效的进行孤立点检测，有效的解决了传统多密度聚类算法中不能有效识别孤立点和噪声的缺陷。
GDD algorithm can not only clusters correctly but find outliers in the dataset, and it effectively solves the problem that traditional grid algorithms can cluster only or find outliers only.
当船漂回大海时，沿岸倒退的针孔般的光点看似靠近地球尽头的孤立的前哨。
As the boat drifts back to sea, the retreating pinpricks of light along the shore resemble isolated outposts somewhere close to the end of the earth.
如果任意两点之间有且只有一条通路，则构成了一棵生成树。即没有网络节点是孤立的。
It's a spanning tree if there's also at least one path between any two points, i.e., no network nodes are left unconnected.
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