Association rules mining is an important model in data mining.
关联规则挖掘是数据挖掘中的一个重要的模型。
The method of association rules mining can be applied to mine CCI;
采用挖掘关联规则的方法可以挖掘出冲突课程集；
For these advantages of rough set, we exert rough set to association rules mining.
有鉴于粗糙集的这些优势，粗糙集理论便被运用于关联规则的挖掘中。
Association Rules mining is the most active one of directions of research on data mining.
而关联规则是数据挖掘中最活跃的研究方向之一。
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。
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。
According to the analysis of association rules mining algorithms, the CPH and AOAA algorithms are presented.
根据对关联规则挖掘算法的分析，提出了CPH算法和AOAA算法。
Introduce the item weights to traditional association rules mining is the expansion in the project properties.
将项目权值引入传统关联规则挖掘中是在项目属性上的扩展。
Therefore, it is necessary apparently that profit constraint in association rules mining has been induced sales.
因此，在关联规则发现中引入销售数量的利润约束问题显得很必要。
In this paper, we discuss the electronic banking application based on association rules mining technology of data mining.
本文主要研究了数据挖掘中关联规则挖掘技术在电子银行业务中的应用。
Non-redundant association rules mining algorithm is proposed to deal with the problem of huge rules' number and redundancy.
针对关联规则数量巨大并且存在极大冗余的问题，提出无冗余告警关联规则产生算法。
Based on the above problem this paper presented a method called association rules mining with fuzzy quantitative constraints.
基于上述问题，提出了一个基于模糊数值约束的关联规则挖掘方法，实际挖掘结果表明这种方法是有效的。
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。
Association rules mining is a key issue in data mining. It discovers and analyses the associations among different sets of data items.
关联挖掘是数据挖掘中的一个重要问题，是发现和分析不同数据项之间的关联性的过程。
One of the important research branches in KDD is the association rules mining. It is therefore significant to investigate this problem.
关联规则的挖掘是知识发现领域重要的研究方向之一，因此开展这方面的研究是很有意义的。
It includes lots of measures such as association rules mining, classification and prediction, clustering analysis and evolvement analysis.
它包含关联规则挖掘、预测、分类、聚类、演化分析等多种技术手段。
This paper firstly presents a power set-based association rules mining algorithm which USES pow er set as an association rules mining tool.
首次提出了利用幂集作为挖掘关联规则的工具，给出了基于幂集的关联规则挖掘算法。
Chapter 6 proposes bidirectional association rules mining and its algorithm, and analyses the relativity of bidirectional association rules.
第六章阐述了双向关联规则挖掘及其算法，并进行了相关性分析。
In allusion to this limitation, how to enhance the efficiency of mining algorithm becomes the core issue of association rules mining research.
针对这一局限性，如何提高挖掘算法的效率就成为了关联规则挖掘研究的核心问题。
Association Rules mining, as an important research subject of data mining field, which plays an important role in many practical applications.
关联规则挖掘作为数据挖掘领域的一个重要研究课题，它在许多实际应用中都发挥着重要作用。
The existing association rules mining algorithms are chiefly based on frequent itemsets, and the record about infrequent itemsets is very rare.
现有关联规则挖掘算法都是在频繁项集基础上进行挖掘，关于非频繁项集的资料很少。
Absrtact: association rules mining is an important branch of research on data mining, its purpose is to find the association or correlation among items.
摘要：关联模式挖掘研究是数据挖掘研究领域的重要分支之一，旨在发现模式之间存在的关联或相关关系。
This paper is focused on the methods of the construction of spatial transaction database, which is a crucial ste Pin the spatial association rules mining.
将空间数据库转换成空间事务数据库是空间关联规则挖掘过程的关键步骤。
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。
Discovering frequent item sets is the main way of association rules mining, and it is also the focus of the study in algorithms for association rules mining.
发现频繁项集是关联规则挖掘的主要途径，也是关联规则挖掘算法研究的重点。
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。
The frequent patterns of texture could be mined by the algorithm of association rules mining. The association rules could be combined to represent the texture.
采用关联规则挖掘算法对图像纹理的频繁模式进行挖掘，通过联合关联规则来表达纹理。
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。
Association rules mining is one of the important functions of data mining, which discovers a set of interesting association from relevant sets of data in a database.
关联规则分析是联机分析挖掘研究的一个重要内容，其目的是找出给定的数据集中的项之间有意义的联系。
Association rules mining is one of the important functions of data mining, which discovers a set of interesting association from relevant sets of data in a database.
关联规则分析是联机分析挖掘研究的一个重要内容，其目的是找出给定的数据集中的项之间有意义的联系。
应用推荐