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。
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。
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。
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。
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。
By combining data gradation, multi-agent communication and knowledge discovery in data-base, the useful information can be extracted from WAMS (Wide-Area Monitoring System).
可利用数据分级整合、多智能体通信以及数据库知识发现等手段相结合实现从广域监控系统(WAMS)海量数据中提取有用的信息。
Applying KDD (Knowledge Discovery in Databases) technology in data analysis is helpful to improve data-analysis ability and productivity in photosensitive materials enterprises.
应用KDD技术进行数据分析，对于提高感光材料企业数据分析水平和生产效率具有积极意义。
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。
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。
The data mining is also called the knowledge discovery in database, which discovers from large quantity of data and find authentic, novel and effective model that can be comprehended by people.
数据挖掘可以称为数据库中的知识发现，它是从大量数据中发现并提取隐藏在其中的可信的、新颖的、有效的并能被人理解的模式的高级处理过程。
Data mining is the core of knowledge discovery in databases. Concept tree method is one of the most important methods. In this paper, presentation of an approach to deal with fuzziness was discussed.
数据采掘是数据库中知识发现的核心，详细描述了数据采掘中概念树方法在模糊性问题中的应用。
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。
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 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。
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。
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。
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。
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。
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。
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。
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。
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。
How to analyze these data to find valuable knowledge and principle has become a hot spot in knowledge discovery.
如何有效地分析利用这些数据，从中发现有价值的知识和规律已成为知识发现领域跨学科研究的热点。
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。
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。
The goal of the ACRG is to improve the knowledge of cancers prevalent in Asia and to accelerate drug discovery efforts by freely sharing the resulting data with the scientific community.
ACRG的目标是通过与科研界免费分享数据来增加亚洲常见癌症的知识并加快药物发现进程。
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。
Rough set, as a theory of data analysis, can deal with uncertainty efficiently , and is one of current hot research directions in knowledge discovery. This paper introduces rough set theory briefly.
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。
Rough set, as a theory of data analysis, can deal with uncertainty efficiently , and is one of current hot research directions in knowledge discovery. This paper introduces rough set theory briefly.
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。
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