Data Mining (DM) is the knowledge discovery from databases.
数据挖掘(DM)是从数据库中发现知识。
Semi-instructured data is a kind of the important type in networks, and its data extracting and knowledge discovery is the core for semi-structured researches.
半结构化数据是网络中一种重要的数据形式，其数据抽取和知识发现研究是半结构化数据各项研究的核心。
Rough Sets theory has great superiority in Data Preprocessing because of its particular expression of knowledge, as well as it makes an effective means of knowledge Discovery in Database.
粗糙集理论由于其独特的知识表示方法在数据预处理方面有着得天独厚的优势，同时也成为数据库中知识发现的有效手段。
Data Mining, also referred to as Knowledge Discovery from database, is to abstract the potential, unknown and useful information or pattern from the large database or data warehouse.
数据挖掘，又称数据库中的知识发现，是指从大型数据库或数据仓库中提取隐含的、事先未知的、潜在有用的信息或模式。
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。
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。
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。
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。
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。
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。
Therefore, it is the same with Data Mining with probability statistic character and knowledge discovery problems, especially with die problems that obtain sample information or need high cost.
因此，适用于具有概率统计特征的数据采掘和知识发现问题，尤其是样本难以获取或代价过于昂贵的问题。
Therefore , it is the same with data mining with probability statistic character and knowledge discovery problems , especially with die problems that obtain sample information or need high cost.
因此，适用于具有概率统计特征的数据采掘和知识发现问题，尤其是样本难以获取或代价过于昂贵的问题。
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。
Rough set theory, a powerful tool to deal with incomplete information, has been widely used in the area of artificial intelligence, especially in data mining and knowledge discovery.
粗糙集理论作为一种处理不完备信息的有力工具，已广泛应用于人工智能的许多领域，特别是数据挖掘和知识发现领域。
However, as well known, there are many issues in databases, such as redundant data, missing data, uncertain data, inconsistent data, and so on, they are the barriers to knowledge discovery.
然而，众所周知，数据库中往往存在冗余数据、缺失数据、不确定数据和不一致数据等诸多情况，这些数据成了发现知识的一大障碍。
Knowledge discovery in databases is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large data set.
数据库中的知识发现是指在大型数据集中识别有效、新奇、潜在有用、且最终可理解模式的非平凡的过程。
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。
Rough set data analysis in the knowledge discovery in database (KDD) is different to other KDD methods, especially with respect to model assumption.
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。
Concept lattice is a powerful tool for concept discovery from data, used to extract hidden knowledge pattern in data.
而概念格正是从数据中进行概念发现的有力工具，用来发现数据中隐藏的知识模式。
This thesis presents its application in spatial data mining and knowledge discovery, and focuses on the cloud models and their algorithms.
针对云理论在空间数据挖掘和知识发现中的应用，提出了基于半云和梯形云的空间距离概念的划分方法。
Data mining, referred to as knowledge discovery in databases, is the extraction of patterns representing valuable knowledge implicitly stored in large databases or data warehouses.
数据挖掘，又称数据库中的知识发现，是指从大型数据库或数据仓库中提取具有潜在应用价值的知识或模式。
Data mining and Knowledge discovery is the technology that can extraction of implicit, previously unknown, and potential useful information from data.
数据挖掘与知识发现技术可以从大量的数据中抽取出隐含的、以往未知而又非常有意义和有用的信息。
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。
Simultaneously, the research development, hot topic and challenges in the filed of data mining and knowledge discovery in database are summarized.
系统地概括了近年来天文学中数据挖掘和知识发现领域研究的进展及其热点，并阐述了其所面临的挑战。
The data model design based on hypergraph has an object-oriented character, which is convenient for setting up models for information knowledge network with respect to opportunity discovery.
基于超图映射的数据模型设计具有面向对象的特性，便于对机遇发现相关的信息知识网络进行建模。
This paper introduces neural networks technology based on data mining and knowledge discovery for inventory problems.
该文介绍了使用基于数据挖掘和知识发现的神经网络技术来解决库存问题的方法。
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。
In this paper, a knowledge discovery model based on data extractor is proposed.
提出了基于数据抽取器的知识发现模型。
Attributeoriented knowledge discovery method is proved to be a powerful approach for mining incomplete data in the large database in our experiment of this paper.
实验证明利用这种基于属性的知识发现方法处理缺损数据是很有效的。
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