Data Mining (DM) is the knowledge discovery from databases.
数据挖掘(DM)是从数据库中发现知识。
Text mining is the kernel of the disjoint literature-based knowledge discovery.
文本挖掘是基于非相关文献知识发现的核心。
It is how to find knowledge from DB that results in knowledge Discovery in Database.
如何从这些海量数据中发现知识，导致了知识发现和数据挖掘领域的出现。
So, knowledge discovery and data mining are proposed with a new study field developed.
因此，知识发现和数据挖掘应运而生，成为一个新的研究领域。
So a reduction algorithm of attribute for personalized knowledge discovery was designed.
为此，设计了一种面向个性化知识发现的属性约简算法。
The process of attribute reduction is analyzed in assembly knowledge discovery based on rough set.
分析了基于粗糙集理论的装配知识发现中属性归约的过程。
Data Mining, also known as knowledge Discovery in Database, distills knowledge from a mass of data.
数据挖掘就是从海量数据中提取知识，又被称为数据库中的知识发现。
Make and have realized the forecast control algorithm that knowledge discovery for theoretical basis.
提出并实现了以知识发掘为理论依据的预测控制算法。
Finally, we design a knowledge discovery system and give examples to testify its validity and exactness.
最后设计知识发现系统的并举例验证了系统的正确性和实用性。
This paper discussed the problem of enterprise marketing knowledge discovery based on network computing.
本文探讨了基于网络计算的企业市场营销知识获取的几个问题。
The model of an idealized knowledge discovery system and its several essential components are introduced.
论述了一种理想化的知识发现系统模型，及其各组成部分的功能。
The significant function of process competition is knowledge discovery undertaken by rivalrous entrepreneurs.
过程竞争的重要功能在于发现知识，争胜竞争的企业家是发现主体。
The knowledge discovery and data mining tool display their strong points in handling the great capacity database.
知识发现及数据挖掘工具在处理海量数据库时显示了它们的长处。
A problem in the information system and knowledge discovery, is a problem of processing optimal knowledge reduction.
最佳知识约简问题是信息系统与知识发现中面临的一个重要问题。
Knowledge discovery process in database is to formalize the knowledge contained in the database into a useful concept.
数据库知识发现的过程就是将数据库中蕴含的知识形式化为有用概念的过程。
Active Spatial data mining technology is used in the processes of alarm data fusion, data mining and knowledge discovery.
其中主动空间数据挖掘技术主要体现在数据融合，数据挖掘和知识发现的过程中。
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。
Data mining is a theory forward in the field of database and decision-making information, It is core of the knowledge discovery.
数据挖掘是数据库和信息决策领域的一个理论前沿，是知识发现的核心部分。
Writer proposes a framework for the automated translation of the output from knowledge discovery systems to active database rules.
作者对知识发现系统的输出提出了一种自动转换到主动数据库规则的结构。
Rough sets theory was used widely to artificial intelligence, pattern recognition, data mining and knowledge discovery etc fields.
粗糙集理论被广泛应用于人工智能、模式识别、数据挖掘和知识发现等领域。
It is a process of spatial knowledge discovery and data mining. It sums up a modeling problem of the GIS-based reservoir evaluation.
其本身就是一个空间知识发现和挖掘的过程，实质可归结为基于GIS的油气储层评价建模问题。
The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.
知识发现的过程包括时间序列数据预处理、属性约简和规则抽取三部分。
Most data mining tools for knowledge discovery generally use rule discovery and decision tree technology to extract data patterns and rules.
用于知识发现的大部分数据挖掘工具均采用规则发现和决策树分类技术来发现数据模式和规则。
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。
In this paper, after making a analysis of the relate field of data mining and its basic questions, we provide a new method for knowledge discovery.
本文分析了数据挖掘技术的相关领域及其基本问题，为知识获取提供了一种新方法。
The advances in the research on the basic theory and practical application of distributed knowledge discovery in the grid environment are expounded.
本文论述了网格环境下的分布式知识发现相关基础理论与实践应用项目的研究进展。
Rough set, as a theory of data analysis, can deal with uncertainty efficiently , and is one of current hot research directions in knowledge discovery.
粗集作为一种数据分析理论，能有效地从不确定性的数据中发现知识，是目前在知识发现领域研究的热点之一。
Affront this challenge, the new problem of knowledge element Semantic Grid of the platform of knowledge service and knowledge discovery is the design.
面对这一挑战，文章提出构建知识元语义网格平台，实现以知识元为知识单位的知识发现服务体系结构。
Main measure indexes of information effectiveness in historical archives are the difficulty of knowledge discovery, demand, accessibility and effects.
历史档案情报效用的主要衡量指标是知识发现难易度、需求量、可获取性和使用效果。
A knowledge discovery method based on analysis results is put forward for discovering underlying design rules and knowledge embedded in analysis results.
为了发现隐含在模拟结果中潜在的规律和知识，提出了一种基于板料成形数值模拟的知识发现方法。
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