为何使用关联规则进行挖掘？
In this case three association rules are discovered.
在本例中发现三条关联规则。
Association rules describe which items often co-occur.
关联规则描述了哪些项目经常同时发生。
A slight variation of association rules are sequential rules.
序列规则是关联规则的一种，与关联规则稍有不同。
Association rules describe events that tend to occur together.
关联规则描述可能一起发生的事件。
This can generate a great many trivial association rules.
这导致大量平凡的规则产生。
The analytical goal is to extract association rules of the form.
此次分析的目的是提取表单的关联规则。
E-commerce sites can clearly benefit from strong association rules.
电子商务站点可以很明显的从强大的关联规则中的受益。
Mining association rules is a major aspect of data mining research.
挖掘关联规则是数据挖掘研究的一个重要方面。
A traditional example of association rules can be seen in the retail sector.
关联规则的传统应用多见于零售业。
Finally, you have to store the extracted association rules to a physical table.
最后，我们还必须将这些提取出的关联规则存储到一个物理表中。
Mining quantitative association rules is an important task of data mining.
量化关联规则的挖掘是数据挖掘的一项重要任务。
You have to perform the following steps to create the association rules report.
为了创建这个关联规则报告，还需执行下面的步骤。
The cursor returns the content of the RETAIL_RULES table containing the association rules.
此指针返回包含有关联规则的 RETAIL_RULES表的内容。
This kind of algorithms provides a new method for reducing the set of association rules.
该类算法的提出，为关联规则的精简提供了一种新的解决方法。
The second report shows the filtered, dynamically created association rules for the selected product.
第二个报告显示了所选产品经过滤的、动态创建的那些关联规则。
The single minimum support degree is used in the existing association rules mining methods mostly.
现有的关联规则挖掘方法中，大多采用单一的最小支持度。
Mining association rules require two pieces of data, the transaction and what was bought in that transaction.
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。
Objective To explore the application of association rules in the analysis of the liver cancer patients data.
目的探讨关联规则方法在肝癌病人资料分析中的应用研究。
Intelligent Miner will derive some association rules you can use to further understand a customer's buying habits.
IntelligentMiner将派生出一些关联规则，您可以使用它们来深入了解顾客的购买习惯。
In this article you learned about association rule mining and how to find association rules with InfoSphere Warehouse.
在本文中，您了解了关联规则挖掘及如何用InfoSphere Warehouse获得关联规则的有关内容。
The conventional framework for mining association rules is the support-confidence framework which has some limitations.
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。
Note, not all products have related association rules, as the support of the rules may have been too small during computation.
请注意，不是所有的产品都有相关的关联规则，因为这些规则的support在计算过程中可能会过小。
Association rules are one of the techniques used in data mining, and particularly useful with e-commerce transactional information.
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。
You have learned that data mining with association rules is a useful way to identify related items in your customers' shopping carts.
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。
After representing brain with a tree-like structure, it USES association rules to min the relations between different brain areas.
将大脑用树状结构表示为各个脑区，然后使用关联规则挖掘出不同脑区之间的关系。
It has been proved by an application of the model that it is valid and effective to discover association rules by using this model.
通过该模型的一个应用实例证明利用这个模型来发现关联规则是可行的、有效的。
Mining association rules is a major aspect of data mining research, and maintaining discovered association rules is of equal importance.
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.
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。
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