然后，在一个挖掘流中使用这个规则文件，把概念从文本列中提取到关系数据库表中。
We will then use this rule file in a mining flow to extract the concepts from text columns in relational database tables.
它还演示了如何组合结构化数据库和文本挖掘。
And it also illustrates how it can be combined with structured databases and data mining.
文本数据挖掘也不同于常规意义上的数据挖掘，常规数据挖掘是在数据库中发现感兴趣的模式，而文本数据挖掘是从自然语言文本中发现模式。
The difference between regular data mining and text mining is that in text mining the patterns are extracted from natural language text rather than from structured databases of facts.
提出广义数据空间的概念，使得数据挖掘能够在数据库或数据仓库的不同部位或不同的抽象级别上，对数字数据或者文本数据进行挖掘，加强了决策分析的功能和灵活性。
A concept of broad sense data space was proposed so that DM can be made in different part and different abstract hierarchy in database and data warehouse, which enhances the functions and flexibility.
目前数据挖掘的对象主要还是本地数据库中的文本及数值类数据，对于图像信息的挖掘所进行的研究还比较有限。
The main objects for data mining remain today to be texts and numerical data in the local database, while studies on image-based data mining appear to be rather limited.
前言： 本研究采用文本挖掘技术，从在全球公开的纳米科学和技术研究文献数据库（SCI/ SSCI数据库）中提取技术情报。
Text mining was used to extract technical intelligence from the global open nanotechnology and nano science research literature.
前言： 本研究采用文本挖掘技术，从在全球公开的纳米科学和技术研究文献数据库（SCI/ SSCI数据库）中提取技术情报。
Text mining was used to extract technical intelligence from the global open nanotechnology and nano science research literature.
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