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。
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。
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。
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。
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。
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 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。
Introducing association rules into the analysis of medical data, making up statistics methods, and getting rich information possibly are the main aim of the study.
将关联规则挖掘引入医学资料的分析之中，弥补传统统计学方法的不足，最大可能地获取数据中含有的信息是本研究的目的。
Association rules are an important aspect of research of Data Minging.
关联规则是数据挖掘研究领域的一个重要分支。
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。
Association rules are an important aspect of research of Data Ming.
关联规则是数据挖掘最重要的课题之一。
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。
When excavating with parallel association rules, the two data distribution characters, data skewness and workload balance, will affect the validity of pruning.
在进行并行关联规则挖掘时，数据偏斜和工作量平衡这两个数据分布特征影响着剪枝的有效性。
Association rules is an important part of data mining research.
关联规则是数据挖掘的重要研究内容之一。
Association rules is an important method of data mining techniques.
关联规则是一种重要的数据挖掘技术。
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。
We may discover strongly related data in product information that we get after selling in marketing by association rules and make product association.
在企业营销中对销售产品信息运用关联规则，发现了具有强关联性的数据后，便可以这些数据为基础完成对企业产品的组合销售。
Mining association rules is an important topic in the data mining research.
关联规则采掘是数据采掘中重要的研究课题。
With the growing concern over data privacy-preserving problem, how to discover association rules from distributed databases becomes one of the hot topics of this field.
数据隐私问题引起人们的广泛关注，如何在分布式数据库的环境下挖掘关联规则成为研究的热点。
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 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。
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。
And making an experiment on it, it proves that binary system sequences set is efficient and feasible as an approach of organization data based on mining of association rules.
通过实验验证，在关联规则数据挖掘中采用二进制序列集这一组织数据方法是有效且可行的。
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。
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。
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