Data mining, also known as knowledge discovery in databases.
数据采掘，也称数据库中的知识发现。
Knowledge discovery in databases is a very active research area.
在数据库中发现知识是一个非常活跃的研究领域。
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。
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。
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。
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。
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, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns.
数据挖掘，或者叫做数据库知识发现，是一种自动在大量数据中寻找具有某种相同属性集合的技术。
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns.
数据挖掘，或者叫做数据库知识发现，是一种自动在大量数据中寻找具有某种相同属性集合的技术。
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