数据归约

数据归约技术及其在IDS中的应用研究

Research on the Data Reduction Techniques and Their Application in IDS

Bug质量与数据归约。

Bug quality and data reduction .

基于数据归约和面向属性归纳的网络流量分析系统

Network Traffic Analysis System Based on Data Reduction and Attribute-oriented Induction

三次样条的全程和局部数据归约策略

Global and local data reduction strategy of cubic spline

适用于时间序列分类的数据归约方法

A New Data Reduction Method for Time Series Classification

粗糙集理论及其在数据归约中的应用

Rough Set Theory and its Use in Data Reduction

数据归约是数据挖掘过程的关键环节，因此对数据归约技术的研究具有重要的意义。

Data reduction is the key step of Data Mining and it is important to study the methods of data reduction .

提出了一种基于密度的孤立点因子算法和一种基于粗集理论的属性类别差异数据归约算法。

The Out-lier algorithm based on density and attribution classical discrepant data protocol algorithm based on rough set theory were presented .

针对中药方剂功效归纳问题，提出了一种基于人工神经网络新的高维数据归约方法。

A novel reduction method of high dimensions based on artificial neural network was proposed for the effect reduction of Chinese traditional medicine prescription .

研究了多层、多属性的归纳。实际数据库中的属性值之间的层次差异较大，需要进行必要的数据归约。

Generalization with multi-level and multi-attribute is studied . There are many differences among attribute values in the practical databases , thus data reduction should be done necessarily .

基于小波理论的方法着重介绍了小波滤波器与线性滤波的关系，将具有线性相位特性的双正交小波9／7小波应用于数据归约中得到了较好的效果。

The 9 / 7 wavelet lifting algorithm , which is the Biorthogonal wavelet has the linear phasic , is applied in the reduction and has a good result .

应用了数据归约技术、聚类的方法、模糊集理论改进了中医药数据的质量，使得在预处理后的中药方剂数据库中成功挖掘出重要规则，为研制中药新药提供了有力的决策支持。

Data reduction technology , clustering analysis and fuzzy set theory are applied to improve the quality of TCM data , getting important rules from the preprocessed TCM database , and providing powerful decision support for exploring new medicine .

针对客户生涯价值分析这一客户关系管理系统的重要问题，在分析已有工作的基础上，经过多级数据归约，提出了多商品配送企业适合工程计算的客户生涯价值公式。

Customer life time value is one of the most important issues in customer relationship management system . Considering existing related work , and by multi-tire data reduction , we proposed a calculating method of customer life time value for multi-commodity distribution enterprise .

借助ORACLE的新特性，以进程流实现对数据的逐步归约。

With ORACLE new features , in order to process the data stream to achieve a gradual reduction .

在实际应用中选取了内蒙古自治区某一个地区的人口作为研究对象，运用预处理技术对数据进行了归约、集成和简化，从而符合数据挖掘的数据要求。

Selected population of a certain area of the Inner Mongolia Autonomous Region in the practical application as the object of study , data reduction , integration and simplify the use of pretreatment technology , thus in line with the data requirements of data mining .

针对遥感图象的海量数据特性指出了图象数据归约的重要性，并提出了两个图象数据归约方法。

The image data reduction is very important in remote sense image processing , two methods in data reduction proposed in this paper , which one is based on wavelet theory and the other is based on clustering analysis .

在挖掘之前，进行了数据清理、数据转换、数据归约等数据预处理工作，通过应用对数据预处理技术有了进一步的认识。

Data cleaning , data conversion , data reduction and other data pre-processing work have been done before mining . And further understanding of data pre-processing technology has been achieved through the concrete application .

分析了包括场致发射有限元分析试验数据结果集获取、数据属性归约等步骤的实现过程，给出了基于熵度量的数据属性集归约和决策树算法原理，从而解决了方案实现的关键问题。

The implementation of the acquisition of the experimental data set , the reduction of the data attribution and other steps was analyzed . The principles of data attribution reduction based on entropy measurement and decision tree algorithm were given to solve the key problems in the scheme .

讨论数据清理、数据集成和变换、数据归约的方法。

Methods of data cleaning , data integration and transformation , and data reduction are discussed .

针对这些数据的不完整性、有噪声性和不一致性，本文用数据清理、数据变换、数据归约等数据挖掘的预处理技术处理这些原始数据。

For the incompleteness , noisiness and inconsistency in these data , we use some preprocess technologies of Data Mining , such as Data Cleaning , Data Transformation and Data Reduction , to process these source data .

建立了多种数据挖掘模型，分别是：（1）聚类模型，主要用于桥梁数据的异常情况监测，数据的归约。

Established several models : ( 1 ) Clustering model , mainly for bridges data anomalies monitoring and data reduction .