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。
Some methods for dividing continuous attributes in KDD (knowledge discovery in database) and a method based on VDM (value difference metric) are presented.
为了提高系统的运行效率，本文还对系统使用的设计模式进行优化，从而达到了有效利用知识发现技术进行客户资料分析的目的。
To improve the efficiently of the system, design patterns are used to optimize system's performance, so the target of mining the customer information by KDD technology is realized.
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。
Fuzzy cluster is one of the branches of knowledge discovery in database (KDD). And neural network is a good tool for clustering.
数据的在线分析与知识发现是开发信息资源的高级形式。
Both online analytical processing (OLAP) and knowledge discovery in database (KDD) are advanced modes of information resources exploitation.
粗集数据分析不同于其它知识发现方法，特别在模型假设方面的一种方法。
Rough set data analysis in the knowledge discovery in database (KDD) is different to other KDD methods, especially with respect to model assumption.
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。
So in facing of challenge of "Much data, but little information", there come Data Mining (DM) and Knowledge Discovery in Database (KDD).
本文在最后还探讨了自动文摘在“知识发现”和文本信息挖掘领域内的初步应用。
In the end, the author discusses the application of automatic abstracting in the KDD (Knowledge Discovery from the Data) field.
针对平台自动测试系统故障诊断的特点和设计要求，提出了一种将知识发现技术融入故障诊断系统中的新的框架，同时设计了知识发现操作的具体过程。
In view of the characters and designing requirements of platform test system fault diagnosis, a new framework of diagnosis system using knowledge discovery in database (KDD) technology is put forward.
但是，有的知识发现技术建立的模型要么比较复杂，要么需要一定的先验知识、具有主观性。
However, some model based on KDD technology is more complicated or needs certain field knowledge, which is subjective.
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。
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.
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。
After analyzing the principle and flow of the arithmetic, application in tobacco sensory evaluation for KDD is achieved.
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。
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.
最后提出一种基于粒度和概念格的知识发现模型。
The KDD model based on the granularity and concept lattice is proposed at last.
在一般意义下，数据挖掘与知识发现的概念一致。
In general, the concept of data mining is the same as that of KDD.
本文介绍了数据仓库、知识发现以及数据挖掘的概念，详细分析了关联规则算法，时纳税人采用的主要违法违章手段之间的关联关系进行了数据挖掘。
In this article we introduced the concepts of DW, KDD and DM, analysed the association rules algorithm and used it into mining the association rules of kinds of tax offence.
介绍了数据库知识发现(KDD)活动的展开要求，着重从它的技术处理流程来分析它的特性及其存在价值与意义。
This article introduces the KDD activity the request that launch, and puts great emphasis on its characteristic and value by technique processing.
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。
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.
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。
So using neural networks, statistics and data extraction to realize intelligent sensory evaluation for KDD will be important and significant.
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。
One of the important research branches in KDD is the association rules mining. It is therefore significant to investigate this problem.
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。
Bayesian network classifier is one of the main research methods in data mining and KDD domain.
目前，在CRM中使用的知识发现系统很多。
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。
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns.
第三章在算法层次上提出了基于自组织竞争神经网络数据挖掘算法的知识发现系统用于生成拟定的结构方案。
Chapter 3 Applies the KDD system whose data-mining algorithm is based on Self-Organizing Neural Network for generating initial alternative designs.
第三章在算法层次上提出了基于自组织竞争神经网络数据挖掘算法的知识发现系统用于生成拟定的结构方案。
Chapter 3 Applies the KDD system whose data-mining algorithm is based on Self-Organizing Neural Network for generating initial alternative designs.
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