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。
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。
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。
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。
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。
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。
All association rules can be derived by covering the smallest association rule set.
所有的关联规则都可以通过覆盖最小的关联规则集得到。
Discusses the application of association rules in coronary heart disease diagnosis.
探讨关联规则挖掘在冠心病诊断中的应用研究。
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 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。
It proposes a design plan to implement the recommendation engine using association rules in the end.
最后提出了使用关联规则实现推荐引擎的设计方案。
The article introduces some research about association rules updating when the database records increase.
论文研究了事务数据库记录增加时关联规则的更新问题。
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。
This paper focuses on discovering the potential attack behaviors by analyzing association rules in audit data.
本文通过对审计数据进行关联规则分析，发现潜在的攻击系统行为。
In this paper, one new maintenance algorithm is presented for the efficient maintenance problem of association rules.
该文提出一个新的维护算法，用来解决关联规则的更新维护问题。
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。
In this paper, one new incremental updating strategy is presented for such efficient maintenance problem of association rules.
提出了一种新的增量更新策略，用来解决这一关联规则的高效维护问题。
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 method of mining normal association rules is also provided. With this method, all interesting normal association rules can be mined.
接着给出正态关联规则的挖掘方法，此方法能挖掘出所有有意义的正态关联规则。
Through defining information granule with binary string, we introduce an algorithm of mining association rules based on granular computing.
通过采用二进制串表示所定义的信息粒，提出了基于粒计算的关联规则挖掘算法。
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。
Association rules mining is an important technique in data mining and KDD, but some problems exist in the association rules mining based on support and confidence.
关联规则挖掘是数据挖掘和知识发现中一门重要技术，但基于支持度-置信度框架的关联规则挖掘存在一些问题。
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。
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。
应用推荐