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。
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。
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 association rules is an important part of data mining.
关联规则的发现是整个数据挖掘课题中的重要组成部分。
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。
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。
Mining for association rules is a simple and very useful data mining method.
关联规则发现是数据挖掘技术的一种简单又很实用的方法。
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 is one of the primary methods used in telecommunication alarm correlation analysis.
关联规则挖掘算法是通信网告警相关性分析中的重要方法。
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。
Study shows that this method presents more flexibility and efficiency than the approach of independent mining association rules.
研究表明，同孤立的关联规则挖掘方法相比，该方法具有较大的灵活性和更高的效率。
Mining association rules can find out some potential correlations in large quantity of data and has been applied widely in some fields.
关联规则挖掘可以发现大量数据项集之间隐含的关系，在许多领域得到了广泛应用。
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。
The results of test show that the improved algorithms are indeed able to effectively find interesting association rules in the practical application.
实验结果表明本文的改进算法确实能够有效地发现实际应用中的有趣关联规则。
Mining association rules in databases is the hot point in people's researches and the application of fuzzy-set theory has added new energy into the field.
数据库中关联规则挖掘一直是人们研究的热点，而模糊集理论的应用又为这一领域注入了新的活力。
Conclusion Applying association rules analysis in the liver cancer patients data could discover some potential, valuable relationships of multi-factors in the data.
结论在肝癌病人资料中应用关联规则分析可以揭示多因素间潜在的、有价值的关系。
You will create two reports — one that shows a revenue list grouped by products and another one that shows the list of association rules on the products from the RETAIL table.
我们将创建两个报告——一个显示依产品分组的收入列表，另一个显示RETAIL表内的产品的关联规则列表。
Finally, learn how to create a simple Cognos report showing a revenue list for the products and allowing to drill-through to the relevant association rules for those products.
最后，我们还要学习如何创建一个简单的Cognos报告来显示产品的收入列表和允许穿透钻取这些产品的相关关联规则。
Finally, learn how to create a simple Cognos report showing a revenue list for the products and allowing to drill-through to the relevant association rules for those products.
最后，我们还要学习如何创建一个简单的Cognos报告来显示产品的收入列表和允许穿透钻取这些产品的相关关联规则。
应用推荐