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.
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。
Results show that the validity of outlier mining algorithm based on swarm intelligence.
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.
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。
This paper discusses the importance and approach of marketing outlier mining under the network computing.
对国内外数据流离群数据挖掘研究情况分析可知，以往的挖掘算法还存在诸多问题。
By analyzing data streams outliers mining situation of foreign and domain, we found that there exist many problems in the previous algorithms for detecting outliers.
针对数据集中离群数据的挖掘速度的问题，提出了快速的基于单元格的离群数据挖掘算法。
The speed of mining outliers from dataset is slow. According to the characteristic of grid, fast outliers mining algorithm was proposed by partitioning the data into a set of units cell firstly.
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.
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.
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.
探讨对挖掘出的离群数据集进行解释与分析的有效方法。
Some efficient methods of explaining and analyzing outliers is discussed in this paper.
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.
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.
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.
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.
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.
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