We will then use this rule file in a mining flow to extract the concepts from text columns in relational database tables.
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。
Data mining seeks to extract patterns from large sets of data using, among other things, statistical methods, artificial intelligence, and standard database management techniques.
数据挖掘旨在使用统计方法、人工智能和标准的数据库管理技术等等，从大型数据集中抽取模式。
Mining is invoked by a stored procedure call and creates an XML mining model in the database.
可以通过一个存储过程调用来调用数据挖掘，数据挖掘将在数据库中创建一个XML 挖掘模型。
To create the association rule mining model and extract the rules to a database table, do the following
要创建关联规则挖掘模型并将这些规则提取到数据库表，可以执行如下操作
On the other hand, if you're working with a huge data set (data mining, or database operations), having access to a much larger data cache may quite easily make up for this.
另外一方面，如果您要处理大量数据集(数据挖掘或数据库操作)，访问更大的数据缓存，那么对于64位模式来说这非常容易。
To call mining from Cognos, it is ideal to do both in a single call to the database.
要从Cognos调用挖掘，最好是在对数据库的单一调用内执行这二者。
InfoSphere Warehouse provides data mining functionality directly in the underlying DB2 database where the data resides.
InfoSphere Warehouse直接在存储数据的底层DB 2数据库中提供数据挖掘功能。
Mining models are stored in the database and can be accessed in a secure, efficient way from cognos.
挖掘模型存储在数据库中，可以从Cognos中安全、有效地访问它们。
The user can interactively invoke mining by calling a corresponding stored procedure on the database from a Cognos report.
从Cognos报告中，用户可以通过调用数据库上相应的存储过程，交互式地调用数据挖掘。
This system adopts data mining technique to obtain useful information and knowledge from existing products database, knowledge storehouse and regulation storehouse to support conceptual design.
本系统采用数据挖掘技术从已有的产品数据库、知识库和规则库中获取有用信息和知识以支持产品概念设计。
Based on introduction in database technology, the paper discusses the data processing technology of goods, especially the data mining technology applying in a high rack warehouse.
在介绍数据库技术的基础上，深入讨论了货物的数据处理技术，尤其是数据挖掘技术在立体仓库中的应用。
The resulting XML data mining model is stored in the database and can be accessed through SQL/XQuery.
这样得到的XML数据挖掘模型存储在数据库中，可以通过 SQL/XQuery进行访问。
InfoSphere Warehouse data mining is built with DB2 stored procedures and user-defined functions for high-performance in-database execution, taking advantage of DB2 as an execution environment.
InfoSphereWarehouse数据挖掘是用DB 2存储过程和用户定义函数构建的，以利用DB 2作为执行环境，从而获得高性能的数据库内执行。
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。
This system adopts data mining technique and obtains useful information and knowledge from existing products database, knowledge storehouse and regulation storehouse to support conceptual design.
采用数据挖掘技术从已有的产品的数据库、知识库、规则库中获取有用的信息和知识来有效地支持机械产品概念设计。
In the 21st century of the knowledge and economic time and facing with the fact of bursting data but poor knowledge, data mining has been put forward and applied in many fields of database management.
21世纪是知识经济的时代，面对数据爆炸而知识贫乏的现实，人们提出数据挖掘思想，并将其广泛应用到数据库管理的各个领域。
Data Mining is one of the international advanced directions in the field of database and information decision.
数据挖掘是目前国际上数据库和信息决策领域的最前沿研究方向之一。
At present, outlier data mining is a hotspot for the researchers of database, machine learning and statistics.
目前，离群挖掘正逐渐成为数据库、机器学习、统计学等领域研究人员的研究热点。
A new sampling method is proposed, which USES the latest technologies of database. It applies classification rule mining, clustering rule and outlier mining to the management of Audit Risk.
一种新的抽样方法是把数据挖掘技术中的分类、聚类及离群点挖掘等应用到审计风险管理中去。
Association rules used in mining the database of tumor diagnoses can provide useful information for tumor diagnoses.
挖掘肿瘤诊断数据库中的关联规则，能为肿瘤诊断提供有用的信息。
Finally, based on researches above, we implement the data mining system of the stock with aggregation database and object oriented technology.
最后，在以上研究的基础上，集合数据库、面向对象等技术实现了股票数据挖掘系统。
Descriptive mining tasks characterize the general properties of the data in the database.
描述性的数据挖掘任务用于特征化数据库数据的一般属性。
Typical data mining approaches look for patterns in a single relation of a database.
传统的数据挖掘算法是在数据库的一张单一的表中查找模式。
Analysis of real-time financial data, process control, rule observance, security application are fields in which data mining based embedded mobile database can be used.
金融数据的实时分析，过程控制，规章的遵守，安全应用这些都是基于嵌入式数据库的数据挖掘可以应用的领域。
Clustering is a data mining problem that has received significant attention by the database community.
聚类作为数据挖掘的一个问题已经受到了数据库团体的密切关注。
The data extracted from the mass of knowledge and information are the major problems in data mining, processing database of scale unceasingly expands, the main methods to information messy.
从海量的数据中提取知识和信息是数据挖掘解决的主要问题、对处理数据库的规模不断扩大，而导致信息杂乱的主要方法。
The importance and practical value to establish a database of mining methods are introduced.
阐述了建立采矿方法数据库的重要性及其实用意义。
Discovering association rules between items in a large database is an important data mining problem as the number of association rule is usually very larger.
在大型数据库项目之间发现关联规则是一个重要的数据挖掘问题，而挖掘出的关联规则数目常常是巨大的。
So data mining for a paging system user database in Guangdong province is presented by concept description method.
为此，论文运用概念描述的方法对广东省某寻呼台的用户资料库进行了数据挖掘。
So data mining for a paging system user database in Guangdong province is presented by concept description method.
为此，论文运用概念描述的方法对广东省某寻呼台的用户资料库进行了数据挖掘。
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