The method for mining association rule is tested to be efficient.
这种方法对于挖掘关联规则是有效的。
Absrtact: Mining association rule is one of the most important topics of data mining.
摘 要：关联规则挖掘研究是数据挖掘研究的一项重要的内容。
You can express each rule found in the data mining as a merchandising association.
您可以将在数据挖掘中发现的每条规则表示成商品销售关联。
Association rule mining is a highly interactive task, and users usually have to try many different parameter Settings to achieve the desired result.
关联规则挖掘是一项高度交互的任务，用户通常需要尝试多种参数设置才能达到理想的结果。
You can use the results from association rule mining to set up bundles of packages that customers tend to buy together.
您可以使用关联规则中规则进行挖掘，然后设置用户有意要一起购买的捆绑包。
To create the association rule mining model and extract the rules to a database table, do the following
要创建关联规则挖掘模型并将这些规则提取到数据库表，可以执行如下操作
Market basket analysis and association rule mining.
市场购物篮分析及关联规则挖掘。
First, learn about the task of association rule mining and how to achieve it in InfoSphere Warehouse.
首先，我们先来了解关联规则挖掘的任务以及如何在InfoSphere Warehouse内实现此任务。
Association rule mining requires the user to state a minimal support and confidence.
关联规则挖掘要求用户要能说明最少support和confidence。
Deploy the association rule mining flow as a DB2 stored procedure.
将关联规则挖掘流部署为DB 2存储过程。
Create a Cognos report using results from dynamic association rule mining.
使用来自动态关联规则挖掘的结果创建一个Cognos报告。
Note, now the association rule mining flow is executing and returning the extracted rules to Framework Manager.
现在，关联规则挖掘流被执行并将所提取的规则返回给Framework Manager。
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。
Association rule mining is invoked by calling a stored procedure as all other mining operations in InfoSphere Warehouse.
对关联规则挖掘的调用是通过调用一个存储过程完成的，与InfoSphere Warehouse内的所有其他挖掘操作无异。
There are many application scenarios in which association rule mining is used.
关联规则挖掘的应用场景很多。
Secondly, it analyzed association rule and sequence mode used in the process of data mining and compared the main algorithms of association rule and sequence mode.
其次，分析了数据挖掘中所使用的关联规则和序列模式，对关联规则和序列模式的各种挖掘算法进行了比较。
A model of data mining is set up after preparation of data by means of attribute structure, and association rule algorithms are carried out. the data mining result is explained and analysed.
采用了属性构造法进行数据预处理，建立了数据挖掘模型，实现了关联规则算法，并对挖掘结果进行解释与分析。
Each mode has its own emphasis, among them, there are some already studied modes have much more research outcome, such as some methods in association rule mining, classification and forecast mode.
各种模式各有侧重，其中有一些已经研究得较为成熟，研究成果也较多，如挖掘关联规则、预测方法和分类模式中的一些其他方法。
Association rule mining which is a method of data mining reveals the latent information and knowledge.
作为一种数据挖掘的方法，关联规则揭示了数据中隐藏的信息和知识。
Several major kinds of data mining methods, including characterization, classification, association rule, clustering, outlier detection, pattern matching, data visualization, and so on.
常用的数据挖掘方法包括描述、分类、关联规则、聚类、孤立点检测、模式匹配、数据可视化等。
Aim To put forward association rule mining algorithm based on relation algebra theory.
目的提出基于关系代数理论的关联规则挖掘算法。
One resolution is that server predicts hot data with the rule discovered in association rule mining and USES data broadcasting technology to push hot data to mobile client.
一种解决方案是服务器根据关联规则挖掘出的规律，对热点数据进行预测，并利用数据广播技术将热点数据不断地推向移动客户机。
The system gives two kinds of recommendation algorithms based on association rule mining and user's transaction pattern clustering.
本系统给出了基于关联规则挖掘和基于用户事务模式聚类两种推荐算法。
Finally, the characteristics and connection strategies of generator are presented, and based on subsume index, a breadth-first algorithm for mining non-redundant association rule is proposed.
最后，讨论了生成子的性质及连接策略，并在包含索引的基础上，给出了一种宽度优先的无冗余关联规则挖掘算法。
The experimental results show that this new algorithm has proven its significant performance in the sparse multidimensional association rule mining.
实验结果表明，新算法在对具有稀疏特性的多维关联规则的挖掘中体现了良好的性能。
Considering the customer relationship management and the customer segmentation theory, this dissertation researches on association rule mining techniques 'application on the customer segmentation.
本文从客户关系管理中客户细分理论出发，研究关联规则挖掘技术在客户细分中的应用。
While they are commonsense, association rule mining can find many other interesting interactions, such as' beer and diaper '.
有趣的是，关联规则挖掘能找到，像“啤酒与尿不湿”被同时销售，这种非常识性知识。
Association rule mining may help making many business decisions such as catalog design, cross-marketing, and loss-leader analysis.
关联规则挖掘可以帮助许多商务决策的制定，如分类设计、交叉购物和贱卖分析。
Association rule mining may help making many business decisions such as catalog design, cross-marketing, and loss-leader analysis.
关联规则挖掘可以帮助许多商务决策的制定，如分类设计、交叉购物和贱卖分析。
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