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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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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