And it also illustrates how it can be combined with structured databases and data mining.
它还演示了如何组合结构化数据库和文本挖掘。
Databases, reporting, data mining, and synchronization are all going to be offered.
数据库、报表、数据挖掘和同步等都会被交付。
They can apply the data mining techniques for very large databases that they learn to research in the medical and biotechnology industries, along with many others.
他们能够将数据挖掘技术应用到医学和生物学领域中的大型数据库中。
My research areas include, but are not limited to, databases, data mining, security, social networks, Internet applications and some mathematics.
我应该会在2010年毕业。我的研究领域包括：数据库、数据挖掘、安全、社会网络、互联网应用以及数学和其他。
Data mining is a hotspot that combines the techniques in databases, artificial intelligence and statistics areas.
数据挖掘融合了数据库技术、人工智能和统计学，是目前的研究热点。
Data Mining is a domain which tries to extract knowledge and interesting information from very large-scale databases. This knowledge is hidden, unknown, but potentially useful.
数据挖掘是从大型数据库的数据中提取人们感兴趣的知识，这些知识是隐含的、事先未知的潜在有用信息。
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.
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。
Data mining is a new emerging area for the research of artificial intelligence and databases, in which incremental updating of association rules is an important research topic.
数据挖掘是当今国际人工智能和数据库研究的新兴领域，而关联规则的更新是数据挖掘的一个重要研究内容。
When mining large databases, the data extraction problem and the interface between the database and data mining algorithm become become issues.
对大型数据库进行数据开采时，数据抽取问题及数据库和开采算法的接口设计就变得十分重要。
Mining similar sequences and similar trends in time-series databases is a novel and important problem in data mining literature.
时间序列数据库中相似序列与相似趋势的挖掘，是数据挖掘领域的一个较新的重要问题。
We use data mining technique to tackle this case acquisition problem and discover cases from agriculture weather databases to establish case base.
我们采用了数据挖掘的技术来处理范例获取的问题，从存储于农业气象数据库中的信息发现范例，以形成范例库。
Mining frequent patterns in transaction databases, time series databases, and many other kinds of databases has been studied popularly in data mining research.
挖掘事务数据库、时间序列数据库中的频繁模式已经成为数据挖掘中很受关注的研究方向。
Data Mining is a domain that tries to extract knowledge and interesting information from very large-scale databases. This knowledge is hidden, unknown, but potentially useful.
数据挖掘是从大型数据库的数据中提取人们感兴趣的知识，这些知识是隐含的、事先未知的潜在有用的信息。
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 techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization.
数据挖掘技术起源于从统计方法，模式识别，数据库，人工智能，高性能和并行计算和可视化。
Therefore, a unified standard of protein-protein interaction databases is in urgent need for collecting, collating the existing data and mining for useful information from them.
因此，目前亟需一个统一且规范的蛋白质相互作用数据库系统来收集和管理这些数据，并从已有的数据中挖掘有用信息。
The classical data mining approaches can only look for patterns in single relation, and it is difficult to look for complex relational patterns which involved in multi-relational databases.
传统的数据挖掘方法只能从单一关系中进行模式发现，而很难在复杂的结构化数据中发现复杂的关系模式。
Data mining technique can mine and discover valuable and hidden knowledge from databases, so it has been widely studied and applied.
数据挖掘技术能从大量数据中挖掘和发现有价值和隐含的知识，因而得到广泛的研究和应用。
Data mining can mine and discover valuable and hidden knowledge from databases for modeling and optimization.
数据挖掘技术能从大量数据中挖掘和发现有价值和隐含的知识，用于建模和优化。
Data mining can mine and discover valuable and hidden knowledge from databases for modeling and optimization.
数据挖掘技术能从大量数据中挖掘和发现有价值和隐含的知识，用于建模和优化。
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