Data Mining (DM) is the knowledge discovery from databases.
数据挖掘(DM)是从数据库中发现知识。
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 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。
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。
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。
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。
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。
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。
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。
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。
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。
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, also known as knowledge discovery in databases.
数据采掘，也称数据库中的知识发现。
Data mining is the discovery of useful and potential knowledge hiding in databases.
数据挖掘主要是用来找出隐藏在数据库当中那些有用的而未被发现的知识。
Data Mining share a wide range of potential commercial applications, knowledge management and knowledge discovery in the study of a promising new areas of application.
数据挖掘有着广泛的商业应用潜能，是知识发现与知识管理研究中的一个很有应用价值的新领域。
Classification rules discovery is a procedure to construct a classifier through studying the training dataset. It is a very important part of data Mining and Knowledge discovery.
分类规则发现则是通过对训练样本数据集的学习构造分类规则的过程，是数据挖掘、知识发现的一个重要方面。
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。
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。
Data mining is the most critical steps in knowledge discovery, but also the technical difficulties in knowledge discovery, is the very active area of research nowadays.
数据挖掘是知识发现中最关键的步骤，也是知识发现技术难点，是目前相当活跃的研究领域。
In our daily life, there are various kinds of time series data, and time series prediction becomes one of the important aspects of data Mining and Knowledge Discovery (DMKD).
在日常生活中广泛存在着各种时间序列数据，发现时间序列知识、对时间序列进行预测正成为数据挖掘与知识发现的重要内容。
Data Mining is recently core technologies for an enterprise to analyze large data-sets, and it is a key step in knowledge discovery process and a database technical further expanding.
数据挖掘是近年来企业用以分析大型数据集的核心技术，是知识发现过程中的关键步骤，是数据库技术的进一步扩展。
Knowledge discovery is to abstract the previously unknown and potentially useful models from a large amount of data, and data mining is an important component in the procedure of knowledge discovery.
知识发现就是从大量的数据中抽取以前未知并具有潜在可用的模式，数据挖掘则是组成知识发现过程的重要环节。
The grid plays an important role in providing a computational support for distributed data mining and knowledge discovery.
网格为分布式数据挖掘和知识发现提供了有效的计算支持。
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