本文研究了一种基于数据关联规则的网上购物系统。
This text studied a kind of Online Shopping System that based on Data Association Rules.
本论文研究了一种基于数据关联规则的人事管理方案。
This thesis discussed a personnel management project which bases on data connection rule.
最后对基于MADSPM模型的流数据关联规则挖掘问题中需注意的一些问题进行了阐述与分析。
Finally the problems in applying the MADSPM model to association rule mining in stream data are discussed and the strategies for solving them are also given.
对零售业销售数据关联规则挖掘算法的关键思想进行了研究，给出了各种提高算法效率的方法以及对规则选择的方法。
The key idea of mining association rules for the basket data is studied and several methods to improve algorithm efficiency and rules selection are given.
本文研究了一种基于数据关联规则的电子商务网站，并详细阐述了系统的设计目标，总体架构及各功能模块的详细设计。
The paper presents e-commerce website based on data association rules, and in detail elaborates system design targets, overall structure and functional modules.
本文研究了一种基于数据关联规则网上书店系统，此方案与现今网上已采用的一些方案相比，具有用户使用更简单、界面更直观等优点。
In this paper, based on a data association rules online bookstore system with the current program has been used in online programs, the user is simpler, more intuitive interface advantages.
关联规则是在数据挖掘中所使用的一种技术，并对电子商务事务信息非常有用。
Association rules are one of the techniques used in data mining, and particularly useful with e-commerce transactional information.
您已经学习到了使用关联规则的数据挖掘是识别出在顾客购物车中的相关条目的有用的方式。
You have learned that data mining with association rules is a useful way to identify related items in your customers' shopping carts.
挖掘关联规则需要两方面的数据，事务及该事务中所包含的信息。
Mining association rules require two pieces of data, the transaction and what was bought in that transaction.
数据质量组件及其所有相关元数据(规则和规则集、指标、绑定、执行历史、文件夹关联，等等)。
Data quality components with all their associated metadata (rules and rule sets, metrics, bindings, execution history, folder associations, etc.)
您可以将在数据挖掘中发现的每条规则表示成商品销售关联。
You can express each rule found in the data mining as a merchandising association.
具体来说，插件将检查与要执行的工作分区关联的某个分区元数据，并基于定义良好的规则将分区路由到特定的端点。
Specifically, the plug-in would examine some partition meta-data associated with the work partition to be executed and, based on well-defined rules, route partitions to specific endpoints.
关联规则是一种十分简单却功能强大的、描述数据集的规则，这是因为关联规则表达了哪些实体能同时发生。
Association rules are a very simple but powerful formalism for rules that describe datasets because they express which entities can occur simultaneously with each other.
数据挖掘通常涉及到一些标准的任务，包括聚集、分类、回归分析和关联性规则学习。
Data mining commonly involves a few standard tasks that include clustering, classification, regression, and associated rule learning.
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
To create the association rule mining model and extract the rules to a database table, do the following
关联性规则学习(Association rule learning)搜寻数据对象之间的关系，以做出预言、定位产品，等等。
Association rule learning searches for relationships between data objects to make predictions, position products, and so on.
举例来说，当数据项在Web页面或5250屏幕中使用时，与数据项相关联的验证规则将触发验证的进行。
For example, validation rules associated with a data item trigger validation to be run whenever the item is used in a Web page or a 5250 screen.
首先，必须创建一个关联规则模型，该模型被存储为PMML，从中可以提取这些规则并将其放入一个数据库表以供日后的Cognos访问。
First, you must create an association rule model which is stored as PMML from which you can extract the rules into a database table for later Cognos access.
挖掘关联规则是数据挖掘研究的一个重要方面。
Mining association rules is a major aspect of data mining research.
量化关联规则的挖掘是数据挖掘的一项重要任务。
Mining quantitative association rules is an important task of data mining.
本论文正是结合处理中医数据的实际需要提出了线性关联规则，从而把药物和药物剂量有机地联系起来。
This thesis brings forward linear association rules combined with practical application so as to deal with TCP and then make medicine have some relations with their dose.
传统的关联规则数据挖掘的支持度-置信度框架存在着弊端。
The conventional framework for mining association rules is the support-confidence framework which has some limitations.
其次，分析了数据挖掘中所使用的关联规则和序列模式，对关联规则和序列模式的各种挖掘算法进行了比较。
Secondly, it analyzed association rule and sequence mode used in the process of data mining and compared the main algorithms of association rule and sequence mode.
关联规则的主要研究对象是交易数据库，其主要目标就是发现交易数据库中交易项目之间是否存在某些关联关系。
Its main research object is the transaction database, its essential target is to discover whether there is certain connection relation between items in transaction database.
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。
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.
挖掘关联规则是数据挖掘研究的一个重要方面，而维护已发现的关联规则同样是重要的。
Mining association rules is a major aspect of data mining research, and maintaining discovered association rules is of equal importance.
基于数据变换法，提出使用高效数据结构即倒排文件的隐私保护关联规则挖掘算法ifb - PPARM。
Based on data-distort method, we propose privacy preserving association rules mining algorithm IFB-PPARM using efficient data structure namely inverted file.
基于数据变换法，提出使用高效数据结构即倒排文件的隐私保护关联规则挖掘算法ifb - PPARM。
Based on data-distort method, we propose privacy preserving association rules mining algorithm IFB-PPARM using efficient data structure namely inverted file.
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