数据采掘，也称数据库中的知识发现。
Data mining, also known as knowledge discovery in databases.
文本挖掘是基于非相关文献知识发现的核心。
Text mining is the kernel of the disjoint literature-based knowledge discovery.
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.
建立基于知识发现和案例推理的危机预警工作流模型；
Propose crisis warning information system management workflow model based on KD and CBR.
为此，设计了一种面向个性化知识发现的属性约简算法。
So a reduction algorithm of attribute for personalized knowledge discovery was designed.
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.
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。
The process of attribute reduction is analyzed in assembly knowledge discovery based on rough set.
这使下一个十年有希望通过这些知识发现越来越多的治疗方法。
This gives hope that such knowledge will find increasing therapeutic application in the next decade.
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, knowledge discovery and data mining are proposed with a new study field developed.
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。
Finally, we design a knowledge discovery system and give examples to testify its validity and exactness.
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 model of an idealized knowledge discovery system and its several essential components are introduced.
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。
A problem in the information system and knowledge discovery, is a problem of processing optimal knowledge reduction.
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。
The knowledge discovery and data mining tool display their strong points in handling the great capacity database.
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。
Data Mining, also known as knowledge Discovery in Database, distills knowledge from a mass of data.
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。
The process of knowledge discovery in time series includes preprocessing of time series data, attributes reduction and rules extraction.
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。
Writer proposes a framework for the automated translation of the output from knowledge discovery systems to active database rules.
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。
It is how to find knowledge from DB that results in knowledge Discovery in Database.
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。
Rough sets theory was used widely to artificial intelligence, pattern recognition, data mining and knowledge discovery etc fields.
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。
Knowledge discovery process in database is to formalize the knowledge contained in the database into a useful concept.
在上述研究工作的基础上，实现了基于多扩展概念格的分类知识发现原型系统。
Based on the work stated above, a prototype system that can utilize multi-extending concept lattice classification knowledge discovery in database is implemented.
本文的研究着重于知识发现在通信行业客户服务文本记录分类这一问题的应用上。
The study of this text emphasizes on the application of knowledge discovery in the classification of textual records on the customer service in the communication industry.
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。
Active Spatial data mining technology is used in the processes of alarm data fusion, data mining and knowledge discovery.
针对复杂不确定性系统特性，将知识发现理论方法与预测理论方法有机结合起来。
The paper focuses on the properties of complex uncertainty system, and associates knowledge discovery theory and methods with prediction theory and methods.
时间序列相似性模式搜索是营销时间序列数据仓库中知识发现领域的一个研究热点。
The similarity pattern query about time series is one of the research hotspots in knowledge discovering in the time series database.
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。
The advances in the research on the basic theory and practical application of distributed knowledge discovery in the grid environment are expounded.
本文提出了知识元标引的新概念，认为知识元标引是实现跨领域知识集成与知识发现的基础。
This paper presents a new idea of knowledge element indexing, which is considered a foundation of knowledge integration and knowledge discovery across domain.
在分析了客户知识发现的含义和客户知识分类的基础上，提出了一种客户知识发现的分析方法。
On the basis of analyzing the customer knowledge discovery and customer knowledge classification, an approach to customer knowledge discovery is presented.
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。
It is a process of spatial knowledge discovery and data mining. It sums up a modeling problem of the GIS-based reservoir evaluation.
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.
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