Discovering the frequent set of item sequences in a transaction database is one of the most important tasks in mining association rules.
最大频繁项目序列集的生成是影响关联规则挖掘的关键问题，传统的算法是通过对事务数据库的多次扫描实现的。
The reasons for many invalid rules in mining association rules are analyzed. The validity is defined and added to the judgment criterion.
针对当前关联规则采掘中可能产生许多无效关联规则的问题，分析其原因，提出在衡量标准中增加有效度，并给出了有效度的定义。
To reduce invalid rules in mining association rules, we have analyzed the reasons and presented to add the effect or the relative confidence in the judgment criteria.
为了减少关联规则挖掘中的无效关联规则，我们分析了其原因，提出了二种改进方法，即在衡量标准中增加影响度或相对置信度。
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。
In the following example, see how to create the mining flow, creating the association model and extracting the rules from it.
下面的示例显示了如何创建挖掘流程，同时创建关联模型并从中提取规则。
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。
Within the report, you can enter parameters to change the mining parameters. In this example, a revenue-per-product list allows you to drill-through to association rules for the specific products.
在本示例中，有一个针对每个产品的收入列表，借助它就能够穿透钻取特定产品的关联规则。
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 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。
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.
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。
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。
In the last, an application instance of association rules in power plant is presented depicting the procedure for implementing data mining and list result data of test.
最后是一个关联规则挖掘在电厂中的应用实例，描述了该数据挖掘实现的过程，最后给出试验数据结果。
Mining spatial association rules can be used to discover the specific spatial relationship between spatial predicate and non-spatial predicate in the spatial database.
空间关联规则挖掘可应用于发现空间数据库中大量空间谓词与非空间谓词之间的特定空间关系。
This paper proposes a rule recycle technique, which reuses the rules deleted in previous data mining by reclaiming and composing them in order to obtain more association rules.
该文利用规则回收技术，以回收组合的方法将已往在挖掘过程中被删除掉的关联规则加以回收利用，从而可以获得更多的关联规则。
Attributes in the database of tumor diagnoses are usually quantitative attributes, so quantitative attribute discretization is a problem of mining association rules.
肿瘤诊断数据库中的属性常为数量型属性，因此如何将数量型属性离散化是挖掘关联规则的难点。
This paper presents a multi-level association rules mining algorithm DMARM which can be used in distributed environments. It can apply different thresholds at different levels.
提出了在分布式环境下对于每一层使用不同支持度的多层关联规则挖掘问题及其算法DMARM。
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。
It can be used in the discovery of non-redundant association rules, sequence analysis, and many other data mining problems.
它可以进一步应用到无冗余关联规则发现、序列分析等许多数据挖掘问题。
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。
In this paper, we combine cluster method with mining association rules.
本文将聚类思想引入到关联规则挖掘中。
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。
Discovering association rules is one of the most important task in data mining.
挖掘关联规则是数据挖掘的一个重要任务之一。
This algorithm has been applied in association rules mining in one marketing EIS and shown that it is practical and effective.
该算法应用于某营销经理信息系统的关联规则挖掘，获得的结果表明算法是实用和有效的。
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。
Mining algorithm and prediction method of fuzzy association rules are discussed in this paper.
讨论了区间值关系数据库上模糊关联规则的挖掘算法与预测方法。
The key problem in distributed association rules mining is to cluster partition in distributed environment.
在分布式关联规则挖掘中，首先需要解决分布式环境下的聚类分区问题。
In the process of association rules mining, the main factor of influencing the mining efficiency is that a large number of candidate items are came into being.
关联规则挖掘过程中，大量候选项集的产生成为影响挖掘效率提高的一个主要因素。
In the process of association rules mining, the main factor of influencing the mining efficiency is that a large number of candidate items are came into being.
关联规则挖掘过程中，大量候选项集的产生成为影响挖掘效率提高的一个主要因素。
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