Cognos is very well-suited to support the task of interactive outlier analysis.
Cognos非常适合分析交互式离群值。
Experimental results show that the approach is scalable and it can efficiently satisfy the demand of real-time outlier analysis.
实验结果表明提出的算法是有效的，可以满足大多数实时性检测与分析要求。
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。
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.
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。
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。
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。
An outlier detection algorithm based on principal component analysis and the sum of attributes distance is proposed.
提出了一种基于主分量分析和属性距离和的孤立点检测算法。
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。
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。
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。
Based on above analysis, a reactive outlier detection approach over data stream has been introduced.
在此基础上，本文提出了一种响应式数据流异常检测方法。
Based on above analysis, a reactive outlier detection approach over data stream has been introduced.
在此基础上，本文提出了一种响应式数据流异常检测方法。
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