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。
Results show that the validity of outlier mining algorithm based on swarm intelligence.
实验结果显示了基于群体智能的离群数据挖掘算法的有效性。
At present, outlier mining has attached a great importance in the field of time series analysis.
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。
The offset-based outlier mining detecting method is a key technique for analysis of outlier mining.
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。
This paper discusses the importance and approach of marketing outlier mining under the network computing.
文章探讨了在网络计算的环境下的市场营销离群数据挖掘的重要性与内容。
It applies classification rule mining, clustering rule and outlier mining to the management of Audit Risk.
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。
One of the difficulties is Outlier Mining, which is partly solved by the new-arising subject, say, statistic diagnosis.
数据挖掘的一个难题是异常挖掘，而另一新兴学科-统计诊断中的相关理论很大程度上解决了这一难题。
It uses local outlier mining method to count the Local Outlier Factor(LOF) of the outlier candidated object and detects anomaly attacks.
采用局部离群挖掘方法计算离群候选对象的离群因子，检测出异常攻击。
Experimental results show that the improved algorithm is effective and efficient in outlier mining and it is faster than the original algorithm.
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。
This paper aims at outlier mining, and proposes an algorithm of outlier mining called AOMGC based on grid clustering techniques, with the existing algorithm of LOF.
针对离群点的挖掘，在现有的LOF算法的基础上，提出了一种基于网格聚类技术的离群点挖掘算法AOMGC。
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。
In chapter 3, we study the influence point mining of linear model under elliptic restriction, and show the corresponding statistical function and the outlier mining algorithm.
第三章研究了椭球约束下线性模型的影响点挖掘，给出了相应的统计量，并设计异常挖掘算法。
The problem of outlier mining has been variously called outlier analysis, anomaly detection, exception mining, detecting rare events, mining rare classes, deviation detection, etc.
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。
But they are blind in how to apply outlier mining into the practical problem and how to be a dber. With the applications in fraud detection, it is necessary to answer the question.
随着异常点挖掘不断用于风险探测，如何把异常点挖掘的理论和实际的行业背景结合，成为了一个重要的课题。
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.
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。
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 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。
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。
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。
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。
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。
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。
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。
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。
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.
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。
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。
This paper presents a Local Outlier Coefficient - Based (LOC) Mining of Outliers.
提出了一种基于局部孤立系数(loc)的孤立点挖掘算法。
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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