数据挖掘中的关联规则方法

介绍了关联规则的基本概念,总结了关联规则的分类及各种挖掘算法,并对一些典型算法进行了介绍,最后展望了关联规则挖掘的下一步研究方向。     

数据挖掘中的关联规则方法

介绍了关联规则的基本概念,总结了关联规则的分类及各种挖掘算法,并对一些典型算法进行了介绍,最后,展望了关联规则挖掘的下一步研究方向。     

关联规则挖掘算法及优化策略研究

关联规则挖掘是数据库中知识发现研究的热点课题,有着广泛的应用领域.通过对关联规则中快速开采算法的研究分析,首先把已有的关联规则挖掘算法分为了两大类:传统类型的关联规则挖掘算法和多关系关联规则挖掘算法;重点分析基本类型算法,并提出各种改进的优化策略;然后对各类代表性算法进行了描述,分析和对比;最后,对尚存在的问题进行了分析和总结.     

OLAP关联规则挖掘

该文提出一种新的关联规则挖掘方法,OLAP关联规则挖掘。OLAP关联规则挖掘是OLAP技术和一些高效的关联规则挖掘算法的结合。OLAP关联规则挖掘方法是一种灵活的、多维的、多层次的高性能方法。该文首先介绍了O-LAP关联规则挖掘的结构,最后详述了OLAP关联规则挖掘的具体实现。     

基于Rough Set带结论域的关联规则挖掘

论文构建了一种基于RoughSet(RS)带结论域的强关联规则挖掘模型,采用约简决策表和改进的Apriori算法来挖掘关联规则,提高了关联规则的挖掘效率和挖掘质量,提出并实现了带结论域的关联规则挖掘的解决方案。     

一种新的关联规则挖掘方法

关联规则挖掘是数据挖掘的主要任务之一。为了进一步提高关联规则挖掘算法的认知特性和运算效果,提出了一种新的关联规则挖掘思想并由此构造了一种基于规则模糊认知图的关联规则挖掘算法。该算法使用规则模糊认知图进行知识表示,对每个挖掘到的关联规则进行可达模糊推理,从而减少了与数据库交互的次数。实验证明该方法与Apriori的关联规则算法相比,提高了关联规则挖掘的效率,增强了智能化程度。     

关联规则挖掘的基本算法

介绍了加权模糊关联规则挖掘算法的基本思想及实现步骤,并给出挖掘算法的多种策略。在此基础上,分析了加权模糊关联规则与模糊关联规则、布尔型属性加权关联规则、布尔型属性关联规则之间的内在联系,并指出加权模糊关联规则挖掘算法是一种最基本的关联规则挖掘算法,蕴涵了其它3种关联规则挖掘算法。     

分布式环境下挖掘约束性关联规则的算法研究

关联规则是数据挖掘的重要研究内容。基于约束的关联规则挖掘可以促进交互式探查与分析。该文主要研究了分布式环境中挖掘约束性关联规则的问题。在并行关联规则挖掘算法CD和约束性关联规则挖掘算法Direct的基础上,提出了一种新的分布式挖掘约束性关联规则算法DMA_IC。该算法对于解决分布式挖掘约束性关联规则的问题是十分有效的。同时,文章还对DMA_IC算法的通信性能进行了讨论。     

基于兴趣度的关联规则挖掘

关联规则挖掘是数据挖掘领域中的重要研究内容之一。然而,传统的基于支持度-可信度框架的挖掘方法可能会产生大量不相关、甚至是误导的关联规则。针对现有关联规则挖掘的评价标准存在的问题,提出在评价标准中增加兴趣度,并给出了兴趣度的定义和基于兴趣度的关联规则挖掘算法。利用兴趣度将关联规则分为正关联规则和负关联规则,从而可以用算法挖掘带有负项的关联规则。实验结果分析表明,在传统挖掘方法的基础上引入兴趣度,可以有效地减少正关联规则的规模,产生有意义的负关联规则。     

基于兴趣度的关联规则挖掘

关联规则挖掘是数据挖掘领域中的重要研究内容之一。然而,传统的基于支持度-可信度框架的挖掘方法可能会产生大量不相关、甚至是误导的关联规则。针对现有关联规则挖掘的评价标准存在的问题,提出在评价标准中增加兴趣度,并给出了兴趣度的定义和基于兴趣度的关联规则挖掘算法。利用兴趣度将关联规则分为正关联规则和负关联规则,从而可以用算法挖掘带有负项的关联规则。实验结果分析表明,在传统挖掘方法的基础上引入兴趣度,可以有效地减少正关联规则的规模,产生有意义的负关联规则。     

Applications of association rule mining in health informatics: a survey

Association rule mining is an effective data mining technique which has been used widely in health informatics research right from its introduction. Since health informatics has received a lot of attention from researchers in last decade, and it has developed various sub-domains, so it is interesting as well as essential to review state of the art health informatics research. As knowledge discovery researchers and practitioners have applied an array of data mining techniques for knowledge extraction from health data, so the application of association rule mining techniques to health informatics domain has been focused and studied in detail in this survey. Through critical analysis of applications of association rule mining literature for health informatics from 2005 to 2014, it has been explored that, instead of the more efficient alternative approaches, the Apriori algorithm is still a widely used frequent itemset generation technique for application of association rule mining for health informatics. Moreover, other limitations related to applications of association rule mining for health informatics have also been identified and recommendations have been made to mitigate those limitations. Furthermore, the algorithms and tools utilized for application of association rule mining have also been identified, conclusions have been drawn from the literature surveyed, and future research directions have been presented.     

关联规则挖掘研究述评

1 引言近年来,数据挖掘(又称为数据库中知识发现,KDD)引起了信息产业界的极大关注。关联规则挖掘作为数据挖掘的一种重要模式,已成为数据挖掘领域的一个非常重要的研究课题。它在商务管理、生产控制、市场分析、工程设计、科学探索等领域都有着重要的应用,目前又逐渐向生物医药、金融分析、电信等领域渗透。     

Positive and negative association rule mining on XML data streams in database as a service concept

In recent years, data mining has become one of the most popular techniques for data owners to determine their strategies. Association rule mining is a data mining approach that is used widely in traditional databases and usually to find the positive association rules. However, there are some other challenging rule mining topics like data stream mining and negative association rule mining. Besides, organizations want to concentrate on their own business and outsource the rest of their work. This approach is named “database as a service concept” and provides lots of benefits to data owner, but, at the same time, brings out some security problems. In this paper, a rule mining system has been proposed that provides efficient and secure solution to positive and negative association rule computation on XML data streams in database as a service concept. The system is implemented and several experiments have been done with different synthetic data sets to show the performance and efficiency of the proposed system.     

基于命题逻辑的关联规则挖掘算法L-Eclat

关联规则挖掘是数据挖掘领域非常重要的课题,在很多领域被广泛应用。关联规则挖掘算法都需要设置最小支持度和最小置信度。很多国内外学者研究的挖掘算法在这两方面都存在着一些问题,不仅需要大量的领域知识来设置合适的最小支持度,而且其结果集庞大、用户不容易理解。针对关联规则挖掘算法存在的问题,将命题逻辑融合到关联规则算法Eclat中,设计出了基于命题逻辑思想的挖掘算法L-Eclat。实验结果表明,L-Eclat算法压缩了挖掘的规则集,减小了算法的时间消耗,且即使是非常小的支持度也可以得到高质量的关联规则,这在一定程度上解决了支持度设置的问题。     

Generalized multidimensional association rules

The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knwledge discovery from large-scale databases.Ande there has been a sput of research activities around this problem.Traditional association rule mining is limited to intra-transaction.Only recently the concept on N-dimensional inter-transaction association rule(NDITAR)was proposed by H.J.Lu.This paper modifies and extends Lu‘s definition of NDITAR based on the analysis of its limitations,and the generalized multidimensional association rule(GMDAR)is subsequently introduced,which is ore general,flexible and reasonable than NDITAR.     

Evolutionary Rule Mining in Time Series Databases

Data mining in the form of rule discovery is a growing field of investigation. A recent addition to this field is the use of evolutionary algorithms in the mining process. While this has been used extensively in the traditional mining of relational databases, it has hardly, if at all, been used in mining sequences and time series. In this paper we describe our method for evolutionary sequence mining, using a specialized piece of hardware for rule evaluation, and show how the method can be applied to several different mining tasks, such as supervised sequence prediction, unsupervised mining of interesting rules, discovering connections between separate time series, and investigating tradeoffs between contradictory objectives by using multiobjective evolution.     

关联规则挖掘技术研究进展*

为帮助人们深入研究关联规则挖掘技术,总结了关联规则的分类方法、评价方法以及相关技术的最新进展,特别是对关联规则的主要算法进行了详细的介绍,并探讨未来的发展方向。该研究比较系统全面,对将来进一步深入分析关联规则挖掘技术具有指导意义。     

基于粗糙集的Web用户行为预测研究

随着互联网的飞速发展和Web应用系统的广泛应用,Web挖掘得到了人们越来越多的研究。从Web日志中发现和分析出用户的有用信息的Web日志挖掘已成为研究热点。很多基于关联规则的方法已经被应用于Web挖掘中。运用基于差别矩阵的粗糙集提取Web日志中的关联规则,并将生成的关联规则集用于用户行为的预测。实验结果说明该方法的有效性和实用性。     

Mining the optimal class association rule set

We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less rime than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.     

Mining fuzzy association rules from questionnaire data

Association rule mining is one of most popular data analysis methods that can discover associations within data. Association rule mining algorithms have been applied to various datasets, due to their practical usefulness. Little attention has been paid, however, on how to apply the association mining techniques to analyze questionnaire data. Therefore, this paper first identifies the various data types that may appear in a questionnaire. Then, we introduce the questionnaire data mining problem and define the rule patterns that can be mined from questionnaire data. A unified approach is developed based on fuzzy techniques so that all different data types can be handled in a uniform manner. After that, an algorithm is developed to discover fuzzy association rules from the questionnaire dataset. Finally, we evaluate the performance of the proposed algorithm, and the results indicate that our method is capable of finding interesting association rules that would have never been found by previous mining algorithms.