The KDD model based on the granularity and concept lattice is proposed at last.
最后提出一种基于粒度和概念格的知识发现模型。
Results of the experiment using KDD 99 indicate the effectiveness of the algorithm.
采用KDD 99数据集进行试验，验证了该算法的有效性。
This paper introduces an active idea into KDD and discusses its design and implementation.
介绍了一种主动式KDD系统的思想及其设计与实现。
Along with the deepening of research on KDD, the application of KDD has become very broad.
随着KDD技术研究的不断深入，KDD的应用领域也越来越广泛。
In the data integration stage of KDD, data discretization is one of the most important jobs.
在KDD的数据集成阶段，数据离散化是其中一件非常重要的工作。
Bayesian network classifier is one of the main research methods in data mining and KDD domain.
贝叶斯网络分类器是数据挖掘与知识发现领域研究的主要方法之一。
Preprocessing is a key step of KDD. Favorable preprocessing can improve efficiency of data mining.
数据预处理是KDD的关键一步，良好的数据预处理可以极大地提高数据挖掘的效率。
After analyzing the principle and flow of the arithmetic, application in tobacco sensory evaluation for KDD is achieved.
在分析了该算法的原理、流程之后，将其应用在卷烟的感觉评估领域，实现了烟草数据知识发现。
This article mainly introduced the background, the definition, the processes and the application prospect of KDD technology.
本文主要介绍了KDD技术的产生背景、定义、处理过程及其应用前景。
Fuzzy cluster is one of the branches of knowledge discovery in database (KDD). And neural network is a good tool for clustering.
模糊聚类是目前知识发现(KDD)领域中的研究分支之一，而神经网络是用于聚类的良好工具。
One of the important research branches in KDD is the association rules mining. It is therefore significant to investigate this problem.
关联规则的挖掘是知识发现领域重要的研究方向之一，因此开展这方面的研究是很有意义的。
So in facing of challenge of "Much data, but little information", there come Data Mining (DM) and Knowledge Discovery in Database (KDD).
因此，面对“数据丰富，但信息贫乏”的挑战，数据挖掘和知识发现技术应运而生。
Both online analytical processing (OLAP) and knowledge discovery in database (KDD) are advanced modes of information resources exploitation.
数据的在线分析与知识发现是开发信息资源的高级形式。
So using neural networks, statistics and data extraction to realize intelligent sensory evaluation for KDD will be important and significant.
因此，借助神经网络、统计分析、数据挖掘等技术手段实现智能感觉评估中的知识发现，将具有重要的现实意义。
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns.
数据挖掘，或者叫做数据库知识发现，是一种自动在大量数据中寻找具有某种相同属性集合的技术。
KDD and data mining was used in agriculture, but for the characteristics of agriculture area, normal data mining methods can't apply efficiently.
KDD和数据挖掘技术在农业中得到应用，由于农业领域本身的特点，通常的数据挖掘技术得不到有效应用。
Some methods for dividing continuous attributes in KDD (knowledge discovery in database) and a method based on VDM (value difference metric) are presented.
介绍了在数据库知识发现(KDD)中将连续属性离散化的一些方法，并提出使用值差分度量离散化的算法。
KDD CUP '99 is adopted in experiment and the result shows the model gets the lower false negative rate and false positive rate besides the higher accuracy.
采用KDD CUP99数据集进行测试实验，结果表明：该模型能够获得较高的检测正确率，同时具有较低的漏报率及误报率。
Rough set theory is one of the most prosperous tools and theories that have already been successfully applied to resolve some specific problems in KDD process.
众多的理论和工具都已经成功地应用于解决KDD过程中的某些具体问题，粗集理论是其中最具发展前景的工具之一。
Association rules mining is an important technique in data mining and KDD, but some problems exist in the association rules mining based on support and confidence.
关联规则挖掘是数据挖掘和知识发现中一门重要技术，但基于支持度-置信度框架的关联规则挖掘存在一些问题。
Data mining is an important problem in KDD, and Rough set as a theory of set with fuzzy boundary is widely applied to infer classification rules from decision system.
数据挖掘是知识发现领域的一个重要问题，粗糙集理论是一种具有模糊边界的数据挖掘方法，它被广泛应用于决策系统的分类规则提取中。
The simulations on the KDD CUP99 dataset show that the detection rate of known attacks and unknown attacks and the false positive rate of this algorithm are excellent.
在KDDCUP99数据集中进行了对比仿真实验，实验结果表明算法对已知攻击和未知攻击的检测率以及算法的误警率都是理想的。
Based on cognitive psychology features, heuristic coordinator and maintenance coordinator were proposed for the first time to improve the ability of self-cognition of KDD.
为提高KDD自认知能力，首次提出了基于认知心理特征的启发协调器和维护协调器。
The model automatically checks and merges the newly discovered patterns of KDD process with the original fuzzy knowledge base, thus improves the quality of the knowledge base.
其特点是利用KDD过程发现的知识，与知识库中的原有知识自动进行知识检测与知识融合，自动完善知识库。
Applying KDD (Knowledge Discovery in Databases) technology in data analysis is helpful to improve data-analysis ability and productivity in photosensitive materials enterprises.
应用KDD技术进行数据分析，对于提高感光材料企业数据分析水平和生产效率具有积极意义。
Applying KDD (Knowledge Discovery in Databases) technology in data analysis is helpful to improve data-analysis ability and productivity in photosensitive materials enterprises.
应用KDD技术进行数据分析，对于提高感光材料企业数据分析水平和生产效率具有积极意义。
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