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 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。
A rough rules mining approach based on variable precision rough set theory is proposed in this paper.
提出了一种基于变精度粗糙集理论的规则挖掘算法。
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。
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。
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。
So, along with further studies of RS, decision rules mining in decision table by using RS has become a hotspot.
因此，随着对粗糙集理论的深入研究，利用粗糙集进行决策表中的决策规则挖掘便成了一个热点课题。
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。
Asociation rules mining is one of the important data mining models, which has important theoretical and broad application prospects.
其中关联规则挖掘是数据挖掘的一个重要模式，具有重要的理论价值和广泛的应用前景。
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。
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。
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。
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。
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。
On the one hand, the associated relationship between condition attributes can be found out through association rules mining, in order to weaken the independent assumption.
一方面，通过对关联规则的挖掘，发现条件属性之间的关联关系，并且利用这种关联关系弱化朴素贝叶斯的独立性假设；
This algorithm first produces frequent items using previous common association rules mining algorithm, then produces weighted frequent items from the previous frequent items.
该算法首先利用普通的关联规则算法产生频繁集，然后在该频繁集的基础上产生加权频繁集。
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。
This method conquers the disadvantage of traditional association rules mining methods, mining rules while mining frequent-item set, so the mining efficiency is greatly enhanced.
该方法克服了传统关联规则挖掘方法的不足，在产生频繁项集的同时进行规则挖掘，从而提高了挖掘效率。
In this paper, project background of current mining is introduced, and the key issues and situation at home and abroad of association rules mining technology have been specified.
在本文中，首先介绍了本次挖掘的项目背景，并阐述了关联规则挖掘技术的国内外现状和研究的关键问题。
The current algorithms of parallel association rules mining are analyzed, and the main factors affecting the performance of the mining algorithm in the multi-processor system are discussed.
文章在分析已有并行关联规则挖掘算法的基础上，讨论了多处理器系统中影响并行关联规则挖掘算法性能的主要问题。
The current algorithms of parallel association rules mining are analyzed, and the main factors affecting the performance of the mining algorithm in the multi-processor system are discussed.
文章在分析已有并行关联规则挖掘算法的基础上，讨论了多处理器系统中影响并行关联规则挖掘算法性能的主要问题。
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