模糊关联规则及挖掘算法

本文提出了模糊关联规则的概念,研究了模糊关联规则的性质和挖掘算法,同时还提出了一种新的规则有越性的度量函数。     

一种基于多维集的关联模式挖掘算法

大多数维间关联规则挖掘算法如基于数据立方体的关联规则挖掘算法都假定对象的属性取值只具有单值性．将对象的属性取值扩展到多值，据此提出多维集的概念和基于多维集关联规则的语义特征．在此语义特征下，提出了一个多维集的关联规则挖掘算法．该算法利用多维集关联规则的限制特征，能够在数据集缩减的同时进行侯选集的三重剪枝，因此，具有比直接使用apriori等算法更好的性能，分析了算法的性能和正确性、完备性，并通过实验对算法有效性进行了对比．     

一种用于挖掘正、负关联规则的改进Apriori算法

本文提出一种传统的关联规则挖掘主要着眼于正关联规则，即形如A→B的规则的挖掘，而对负关联规则的研究非常有限，然而实践表明在关联规则的各个应用领域中，负关联规则同正关联规则有着同样的重要性。Apriori算法是挖掘关联规则的一个经典算法，但是它只局限于挖掘正关联规则，本文对该算法进行改进提出了Ex-Apriori算法，新算法不仅能挖出负关联规则，而且由于兴趣度的引进，能够剔除大量无趣的关联规则。实验表明该种算法有效且可行。     

基于规则量和提取率的关联规则挖掘算法

基于规则量和提取率度量标准,提出一种使用并行克隆退火遗传策略的关联规则挖掘算法。该算法结合了遗传算法、模拟退火算法和免疫克隆算法的优点,采用克隆、变异和交叉操作获取问题的最优解。理论分析和仿真实验结果表明,该算法能高效、快速地解决关联规则挖掘问题。     

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。     

最简关联规则及其挖掘算法

传统关联规则挖掘算法往往产生过多规则而难以被决策者所采用。针对该问题，文章从应用的角度提出了最简关联规则，其特点是后项只包括一种产品，同时追求规则前项产品项数的最小化，在此基础上给出了一种最简关联规则挖掘算法。利用该算法得到的最简关联规则集包括的规则数量大为减少且能得出与全部关联规则集相同的决策，避免了大量的冗余挖掘，提高了挖掘效率和应用效果。     

一种高效关联规则挖掘EARM算法的研究

针对类Apriori关联规则挖掘算法的不足，将以往关联规则算法予改进，提出一个更有效率的关联规则挖掘算法EARM算法。通过试验评估，该文所提算法的挖掘效率比Apriori及其改良算法要快2到5倍。     

基于艾宾浩斯遗忘曲线的零售商品模糊关联分析

针对消费者对商品的偏好存在时序变化特征,而传统关联规则方法未考虑时间因素的影响,且对海量数据集进行关联挖掘时存在效率低下的问题,提出了基于艾宾浩斯遗忘曲线的模糊关联规则算法。该方法通过FCM聚类算法对商品进行聚类,并用艾宾浩斯遗忘曲线来修正聚类的距离度量方法,从而得到商品类及各类的代表点商品;然后将各代表点商品作为属性,消费记录小票作为样本,利用模糊关联规则算法得到代表点商品间的规则;最后将某大型超市一个月的销售记录作为关联规则的事务数据来挖掘潜在规律,结果显示所提算法先对商品模糊关联分析,与传统直接对商品进行关联分析相比,该算法可以显著提高关联挖掘的效率和规则的正确率。     

基于最大间隔的基因表达规则筛选

通过基因表达数据发现与特定疾病相关的基因表达规则,对于疾病辅助诊断有重要意义。针对现有关联规则兴趣度度量的不足,提出了基于最大间隔的基因表达规则筛选策略。该筛选策略综合考虑了基因表达规则与同类及异类样本的距离,具有较强的基因表达规则筛选能力。结合最大间隔准则和递增式关联规则挖掘算法设计的关联规则挖掘算法,能够高效地发现Top-K最大间隔基因表达规则。在实际基因表达数据集上的实验结果,验证了最大间隔基因表达规则筛选策略的有效性和挖掘算法的高效性。     

遗传算法和关联规则算法用于入侵检测系统的研究

基于异常的入侵检测系统常采用关联规则挖掘算法，关联规则算法的最小支持度和最小置信度设置不仅要影响入侵检测系统的检出率和虚警率，还要影响入侵检测系统的负荷。本文提出遗传算法搜寻关联规则算法最小支持度和最小置信度最优的设置范围，为实时的入侵检测系统的关联规则挖掘算法提供参数参考，改善入侵检测系统的实时性，提高检出率，降低虚警率。     

基于GrC的信息系统软规则及其度量关系的研究

粒计算(GranularComputing,简称GrC)是一种新的软计算方法。该文利用信息颗粒的位表示(BitRepresenta-tions)来进行信息系统软规则及其度量之间关系的研究。具体地,首先利用软规则对关联规则、决策规则、函数依赖之间的关系进行了分析,然后对关联规则度量、决策规则度量、外延的函数依赖度量的关系进行了研究,并且建立了这些度量的统一模型。     

Pattern filtering and classification for market basket analysis with profit‐based measures

Market basket analysis is one of the typical applications in mining association rules. The valuable information discovered from data mining can be used to support decision making. Generally, support and confidence (objective) measures are used to evaluate the interestingness of association rules. However, in some cases, by using these two measures, the discovered rules may be not profitable and not actionable (not interesting) to enterprises. Therefore, how to discover the patterns by considering both objective measures (e.g. probability) and subjective measures (e.g. profit) is a challenge in data mining, particularly in marketing applications. This paper focuses on pattern evaluation in the process of knowledge discovery by using the concept of profit mining. Data Envelopment Analysis is utilized to calculate the efficiency of discovered association rules with multiple objective and subjective measures. After evaluating the efficiency of association rules, they are categorized into two classes, relatively efficient (interesting) and relatively inefficient (uninteresting). To classify these two classes, Decision Tree (DT)‐based classifier is built by using the attributes of association rules. The DT classifier can be used to find out the characteristics of interesting association rules, and to classify the unknown (new) association rules.     

Ranking discovered rules from data mining with multiple criteria by data envelopment analysis

In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria.     

Ranking discovered rules from data mining with multiple criteria by data envelopment analysis

In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria.     

基于参考度的关联规则挖掘

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参考度,并给出了参考度的定义和基于参考度的关联规则挖掘算法。利用参考度将关联规则分为正关联规则、负关联规则和无效关联规则,从而可以用算法挖掘带有负项的关联规则。最后给出了新算法的实验分析。     

关联规则的综合评价方法研究与实例验证

分别讨论了关联规则的客观评价指标(支持度、可信度和相关强度)和主观评价指标(新颖性、用户感兴趣性和简洁性)，在此基础上给出了一种关联规则的综合自动评价方法，并结合实例验证了该方法的有效性。     

数据库中的模糊逻辑规则发现

关联规则是数据库中的知识发现（KDD）领域的重要研究课题。模糊关联规则可以用自然语言来表达人类知识,近年来受到KDD研究人员的普遍关注。但是,目前大多数模糊关联规则发现方法仍然沿用经典关联规则发现中常用的支持度和置信度测度。事实上,模糊关联规则可以有不同的解释,而且不同的解释对规则发现方法有很大影响。从逻辑的观点出发,定义了模糊逻辑规则、支持度、蕴含度及其相关概念,提出了模糊逻辑规则发现算法,该算法结合了模糊逻辑概念和Apriori算法,从给定的定量数据库中发现模糊逻辑规则。     

数据挖掘中关联规则的有趣性研究

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兴趣度的评估，过去的研究发现，在实际应用中往往很容易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感兴趣的，本文对规则的兴趣度度量的两个方面作了讨论：一个是主观兴趣度度量，另一个是客观兴趣度度量，最后介绍了如何利用模板进行挖掘有趣的规则。     

关联规则相关性的度量

用Apriori算法生成的关联规则包含有无用规则，甚至误导规则。为了使生成的规则更有效，引入了统计学中的卡方检验从统计意义上检验规则是否关联，并找到卡方检验值与相关系数的数量关系，实现了两种方法的统一，并用基于相关系数的算法去生成关联规则。     

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.