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。     

基于多维关联规则兴趣度的问卷调查规则提取

基于能同时处理多个属性间关联关系的多维关联规则算法,对大学生社交网络行为习惯的调查问卷进行研究分析,发现依靠支持度和置信度的关联规则算法有时会产生误导性的结果。针对关联规则存在的这一问题,给出了带有负向的关联规则兴趣度的解决办法,并发现兴趣度规则中减少关联规则计算量的性质,可极大提高了多维关联规则兴趣度算法在规则提取中的效率。实验结果表明,负向的关联规则置信度强于正向的关联规则置信度,引入兴趣度的多维关联规则算法的准确度更高。     

关联规则兴趣度研究

关联规则是一种常见的知识表达形式。本文介绍了关联规则的提取模式和基于PS架构提取模式的不足;介绍了关联规则兴趣度的定义,包括客观兴趣度和主观兴趣度以及综合兴趣度。     

基于兴趣度的时态关联规则挖掘算法

关联规则挖掘是数据挖掘研究中的一个重要方面,给出了一个关联规则中事件所包含信息的模型,在此基础上,提出了一种基于兴趣度的时态关联规则挖掘算法。该算法较好地弥补了应用模板匹配方法筛选基于兴趣度的关联规则时主观性太强,兴趣度阈值的定义过于简单,所挖掘出来的关联规则没有体现时态约束的缺陷。最后,将该算法应用到了股票数据的趋势挖掘中,得到了满意的实验结果。     

兴趣度在增量的关联规则挖掘中的研究

一般的关联规则发现算法使用的都是支持度、置信度框架.但是在增量的数据挖掘过程中,该类算法却需要不断改变支持度、置信度,使得算法本身效率下降,并缺乏可说服性,比如Apriori算法.为了解决该类问题,使用兴趣度框架对增量的数据进行了关联规则挖掘,比较了基于支持度、置信度框架的算法(如Apriori,FUP算法)和基于兴趣度的算法之间的优缺点.试验结果表明:兴趣度能够有效地筛选关联规则,在进行增量的数据挖掘的情况下得到的关联规则总是小于等于支持度、置信度(Aprori)算法挖掘出的规则.     

基于模糊关联规则的微博用户潜在兴趣发现

针对微博用户兴趣随时间变化的特征,提出一种基于模糊关联规则的潜在兴趣发现方法(PIDFAR),利用LDA主题模型表达微博主题分布,通过时间加权的方式计算出用户现在兴趣的主题分布,进行模糊关联规则挖掘,得出关联规则集合以表示和发现用户兴趣随时间发生变化的一般规律,最后根据关联规则集合中关联规则和用户现在兴趣的主题分布来计算相似度,取相似度较高的关联规则的后项的集合组成用户的潜在兴趣.实验表明, PIDFAR方法能够使得用户潜在兴趣的发现过程脱离用户的好友群体限制,相比基于协同过滤技术的潜在兴趣发现方法明显提高了发现微博用户潜在兴趣的准确率.     

基于兴趣度的Web页面关联规则的研究

随着互联网上的信息迅速增长,如何快速准确地寻找到信息越来越受到人们的重视。文中给出了几种计算用户兴趣度的方法,并利用其中一种计算用户兴趣度的方法,论述了基于兴趣度的Web页面关联规则。论述了关联规则和一般的Apriori算法,并利用了"壹支持数下K—关联规则",对一般的Apriori进行了改进,主要是将兴趣度用于Apriori算法中。实验结果证明,该方法用于在网上寻找用户感兴趣的信息具有较好的准确率。     

基于兴趣度的Web页面关联规则的研究

随着互联网上的信息迅速增长,如何快速准确地寻找到信息越来越受到人们的重视.文中给出了几种计算用户兴趣度的方法,并利用其中一种计算用户兴趣度的方法,论述了基于兴趣度的Wed页面关联规则.论述了关联规则和一般的Apriori算法,并利用了"壹支持数下K-关联规则",对一般的apriori进行了改进,主要是将兴趣度用于Apriori算法中.实验结果证明,该方法用于在网上寻找用户感兴趣的信息具有较好的准确率.     

关联规则挖掘综述

简要论述了关联规则挖掘的研究情况,给出了关联规则的分类方法,分析和评价了关联规则的一些典型算法,指出了关联规则的兴趣度,最后提出了关联规则研究的发展趋势。     

影响关联规则挖掘的有趣性因素的研究

关联规则挖掘是数据挖掘研究中的一个重要方面,而其中一个重要问题是对挖掘出的规则的感兴趣程度的评估。实际应用中可从数据源中挖掘出大量的规则,但这些规则中的大部分对用户来说是不一定感兴趣的。关联规则挖掘中的有趣性问题可从客观和主观两个方面对关联规则的兴趣度进行评测。利用模板将用户感兴趣的规则和不感兴趣的规则区分开,以此来完成关联规则有趣性的主观评测;在关联规则的置信度和支持度基础上对关联规则的有趣性的客观评测增加了约束。     

关联规则兴趣度问题研究

经典的关联规则都是使用基于支持度和可信度的度量标准,但经过实践应用证明存在很多问题。为此,引入兴趣度作为关联规则的新度量标准,阐述当前重点研究的客观兴趣度,对PS公式进行探讨,提出它的优点和不足,在此基础上进行相关改进,克服了可信度与支持度框架的缺陷,具有优化关联规则挖掘的作用。     

On rule interestingness measures

This paper discusses several factors influencing the evaluation of the degree of interestingness of rules discovered by a data mining algorithm. This article aims at: (1) drawing attention to several factors related to rule interestingness that have been somewhat neglected in the literature; (2) showing some ways of modifying rule interestingness measures to take these factors into account; (3) introducing a new criterion to measure attribute surprisingness, as a factor influencing the interestingness of discovered rules.     

基于改进FP树的项项正相关关联规则挖掘

兴趣度量在关联规则挖掘中常用来发现那些潜在的令人感兴趣的模式,基于FP树结构的FP-growth算法是目前较高效的关联规则挖掘算法之一,如果挖掘潜在的有价值的低支持度模式,这种算法效率较低。为此,本文提出一种新的兴趣度量—项项正相关兴趣度量,该量度具有良好的反单调性,所得到的模式中任意一项在事务中的出现均可提升模式中其余项出现的可能性。同时,提出一种改进的FP挖掘算法,该算法采用一种压缩的FP树结构,并利用非递归调用方法来减少挖掘中建立额外条件模式树的开销。更为重要的是,在频繁项集挖掘中引入项项正相关兴趣度量剪枝策略,有效过滤掉非正相关长模式和无效项集,扩大了可挖掘支持度阈值范围。实验结果表明,该算法是有效和可行的。     

I‐prune: Item selection for associative classification

Associative classification is characterized by accurate models and high model generation time. Most time is spent in extracting and postprocessing a large set of irrelevant rules, which are eventually pruned. We propose I‐prune, an item‐pruning approach that selects uninteresting items by means of an interestingness measure and prunes them as soon as they are detected. Thus, the number of extracted rules is reduced and model generation time decreases correspondingly. A wide set of experiments on real and synthetic data sets has been performed to evaluate I‐prune and select the appropriate interestingness measure. The experimental results show that I‐prune allows a significant reduction in model generation time, while increasing (or at worst preserving) model accuracy. Experimental evaluation also points to the chi‐square measure as the most effective interestingness measure for item pruning. © 2012 Wiley Periodicals, Inc.     

Behavior-based clustering and analysis of interestingness measures for association rule mining

A number of studies, theoretical, empirical, or both, have been conducted to provide insight into the properties and behavior of interestingness measures for association rule mining. While each has value in its own right, most are either limited in scope or, more importantly, ignore the purpose for which interestingness measures are intended, namely the ultimate ranking of discovered association rules. This paper, therefore, focuses on an analysis of the rule-ranking behavior of 61 well-known interestingness measures tested on the rules generated from 110 different datasets. By clustering based on ranking behavior, we highlight, and formally prove, previously unreported equivalences among interestingness measures. We also show that there appear to be distinct clusters of interestingness measures, but that there remain differences among clusters, confirming that domain knowledge is essential to the selection of an appropriate interestingness measure for a particular task and business objective.     

挖掘所关注规则的多策略方法研究

通过数据挖掘,从大型数据库中发现了大量规则,如何选取所关注的规则,是知识发现的重要研究内容。该文研究了利用领域知识对规则的主观关注程度进行度量的方法,给出了一个能够度量规则的简洁性和新奇性的客观关注程度的计算函数,提出了选取用户关注的规则的多策略方法。     

Standardising the lift of an association rule

The lift of an association rule is frequently used, both in itself and as a component in formulae, to gauge the interestingness of a rule. The range of values that lift may take is used to standardise lift so that it is more effective as a measure of interestingness. This standardisation is extended to account for minimum support and confidence thresholds. A method of visualising standardised lift, through the relationship between lift and its upper and lower bounds, is proposed. The application of standardised lift as a measure of interestingness is demonstrated on college application data and social questionnaire data. In the latter case, negations are introduced into the mining paradigm and an argument for this inclusion is put forward. This argument includes a quantification of the number of extra rules that arise when negations are considered.     

Exploration of Ordinal Data Using Association Rules

The discovery of association rules is a very efficient data mining technique that is especially suitable for large amounts of categorical data. This paper shows how the discovery of association rules can be of benefit for numeric data as well. Based on a review of previous approaches we introduce Q2, a faster algorithm for the discovery of multi-dimensional association rules over ordinal data. We experimentally compare the new algorithm with the previous approach, obtaining performance improvements of more than an order of magnitude on supermarket data. In addition, a new absolute measure for the interestingness of quantitative association rules is introduced. It is based on the view that quantitative association rules have to be interpreted with respect to their Boolean generalizations. This measure has two major benefits compared to the previously used relative interestingness measure; first, it speeds up rule extraction and evaluation and second, it is easier to interpret for a user. Finally we introduce a rule browser which supports the exploration of ordinal data with quantitative association rules.     

关联规则的鲁棒性分析与应用

首先对采用支持度、置信度和改善度阈值来选择关联规则的不足进行了分析,然后引入规则的鲁棒性分析,提出了基于lift 条件下的关联规则鲁棒性判断,能够在若干规则中选择出鲁棒性高的规则,提供决策参考,最后给出了实例应用。