Outlier data mining is an important embranchment in data mining research.
离群数据挖掘是数据挖掘研究的一个重要分支。
Outlier data mining can help people discover the true and unexpected information.
离群数据的发现，往往可以使人们发现一些真实的、但又出乎意料的知识。
The method can be used to filtrate the outlier data and discover clusters of arbitrary shape.
这种方法可以用来过滤“噪声”孤立点数据，发现任意形状的簇。
This will help to summarize information and still allow for a fast access to relevant outlier data records.
这将有助于总结信息，同时允许快速访问相关的异常数据记录。
The recognition of massive outlier data is a problem with a large number of operations in data processing.
批量异常数据的识别是数据处理中的大计算量问题。
Based on the project background, an improved outlier data mining algorithm for time series data is given out.
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。
At present, outlier data mining is a hotspot for the researchers of database, machine learning and statistics.
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。
This article promoted outlier data mining algorithms based on weighted fast clustering to inspect and deal with outlier data effectively.
设计了基于加权快速聚类的异常数据挖掘算法，以便能快速发现异常数据。
Through researching various visualization methods on data mining, we propose a novel interactive visualization outlier data mining method.
通过研究数据挖掘中的各种可视化方法，提出了一种新颖的交互式可视化例外数据挖掘方法。
Data is one of critical resources of information era. It's quality has important influence on the result of intelligent data analysis, especially the emergence of outlier.
作为信息时代关键性资源之一的数据，其质量问题尤其是异常数据的出现对智能数据分析的结果产生越来越重要的影响。
Outlier testing is a statistical procedure for identifying from an array those data that are extreme.
逸出值检验是从一组数据中识别出极端数据中的统计方法。
In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
Outlier detection is a very important technique in data mining.
离群点发现是数据挖掘的一项重要技术。
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.
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。
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.
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。
The accuracy of sensor data is a critical index to evaluate the performance of Wireless sensor Network (WSN). Outlier detection is a crucial but challenging issue for WSN.
数据的准确性是衡量无线传感器网络(wsn)性能的重要指标，异常数据检测是无线传感器网路面临的关键问题和主要挑战。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
常用的数据挖掘方法包括描述、分类、关联规则、聚类、孤立点检测、模式匹配、数据可视化等。
Analysis of outlier mining is one of the important problems in data mining.
孤立点分析是数据挖掘中的一个重要课题。
Data Snooping or outlier detection is a main research subject in measurement data processing and measurement quality controlling.
粗差探测是测量数据处理、测量质量控制的重要研究主题之一。
Research on clustering analysis and outlier detection algorithms has become a highly active topic in the data mining research.
聚类及孤立点检测算法研究已经成为数据挖掘研究领域中非常活跃的一个研究课题。
With the wide application of data mining to modern business, the researches of data mining for outlier and influential point have been paid close attention to by economic and statistical circles.
随着数据挖掘技术在现代商业中的广泛应用，对异常点和强影响点的挖掘成了经济、统计等领域广泛研究的课题。
Abstract: spatial outlier detection is a research hotspot in the domain of spatial data mining.
摘要：空间离群模式探测是空间数据挖掘的一个研究热点。
The detection of outlier is very important in chemistry and chemical engineering which emphasizes experimentation and data acquisition.
局外点检测对于注重试验和数据采集的化学化工领域，其重要性不可忽视。
This paper presents an algorithm for outlier detection in distributed data streams.
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。
General approaches for outlier detection need to divide temporal data into sub-sequences so as to reduce complexity.
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。
As a type of one-class classification algorithm, Support Vector Data Description (SVDD) was used to distinguish target objects from outlier objects.
支持向量域数据描述(SVDD)是一种单值分类算法，用于将目标样本与其他非目标样本区分开来。
First of all, the paper will introduce the existing life-time model of data mining, the concepts of outlier and the algorithms for mining outliers.
本文首先简单回顾已有的数据挖掘生命周期模型以及异常点基本概念和挖掘算法。
We study the incremental data mining technology based outlier factor.
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.
本文主要研究了基于孤立点因子的增量式挖掘技术。
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