基于关联规则的建筑学专业教学质量评价方法

为充分发挥教学质量评价在高校中的作用，研究基于关联规则的建筑学专业教学质量评价方法。首先，构建建筑学专业教学质量评价指标体系；其次，基于关联规则挖掘算法，从众多数据中发现数据之间的关联；最后，通过D-S理论确定指标权重，在众多结果中准确获得最终评价结果。实例分析发现：基于关联规则所建立的教学质量评价模型，模拟软件评分与使用本文方法测试的结果相差在0.001～0.009；使用文中方法进行教学质量评价时，平均耗时只需要54.7 s。由此可以看出，关联规则可以提高建筑学专业教学质量评价的有效性。     

关联规则评价指标的研究

关联规则挖掘是数据挖掘中重要的研究课题,对于如何从海量的信息中挖掘出有效的、可信的、可理解的、感兴趣的关联规则来帮助人们进行分析与决策,已经成为迫切需要解决的内容。现有的关联规则评价标准除了不能很好地满足用户需求外,还存在着含义和分类的不清晰性。本文在综合分析了现有的评价指标的基础上,提出了关联规则评价指标体系结构,明确了各评价指标的含义,并从系统论的角度将评价指标划分为基本评价指标、定量评价指标和定性评价指标三类,以帮助人们在应用评价时参考与使用。     

基于客观兴趣度的关联规则评价方法

目前衡量和生成关联规则的主要准则是考虑支持度和置信度阈值,而在实际应用中仅按此准则来挖掘是不够的, 这主要是因为关联规则的评价标准不合理产生的. 针对关联规则评价指标进行了深入的研究, 分析了“支持度-置信度”架构的局限性, 提出了基于相关性的兴趣度的评价指标PS公式, 根据其数学特性指出了它的优点与不足, 为关联规则评价体系的改进奠定了理论基础.     

用户访问模式聚类分析在网页推荐中的应用

在基于Web使用挖掘的推荐系统中，仅采用关联规则挖掘技术的Web推荐系统在预测用户未来浏览模式时很难取得令人满意的结果。该文将聚类分析方法结合关联规则推荐算法，应用于Web日志文件的挖掘，以改进个性化的推荐方法。实验表明，该算法能够显著地改进推荐测度的精确率指标和综合评价指标。     

挖掘支持度和兴趣度最优的数量关联规则

讨论了数量关联规则提取过程中的连续属性离散化方法和规则的有趣性问题，给出了数量关联规则的客观兴趣度的度量函数，提出用模板匹配方法挖掘用户感兴趣的规则，以解决数量关联规则有趣性的主观评测，研究了一种挖掘支持度和兴趣度最优的形如(A∈[v1，v2]∧)C1)推出C2(其中A为连续属性，C1、C2为类别属性)的数量关联规则方法，并将该方法应用于股市行情分析，实验结果表明是非常有效的．     

基于粗糙集和模糊聚类的用户评价分析

本文基于粗糙集理论和模糊聚类的方法对图书馆的用户评价数据进行了分析,旨在寻找用户评价指标之间的关联规则,确定用户评价的关键性指标。     

基于灰色Markov模型动态关联规则的元规则挖掘

介绍了增加了支持度向量和置信度向量两种规则评价指标的动态关联规则,给出了一种基于灰色Markov模型的预测和分析动态关联规则的元规则的方法。此方法在建立灰色模型的基础上应用Markov链理论,实验证明利用此方法挖掘的元规则要优于灰色模型等其他方法。     

AR-Markov模型在动态关联规则挖掘中的应用

针对规则随着时间变化的特点,为规则建立元规则对其支持度和置信度变化趋势的分析和预测模型。通过增加支持度向量和置信度向量这两种规则评价指标,给出了动态关联规则元规则的形式化定义。利用自回归Markov模型对动态关联规则的元规则进行了挖掘,并通过实例证明了该方法的有效性。     

关联规则挖掘概述

论述了关联规则研究情况,给出了关联规则的概念与分类,分析和评价了关联规则的主要挖掘方法与维护方法,最后提出了关联规则研究的发展趋势。     

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。     

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.     

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。     

Automatic image annotation using fuzzy association rules and decision tree

The problem of sharp boundary widely exists in image classification algorithms that use traditional association rules. This problem makes classification more difficult and inaccurate. On the other hand, massive image data will produce a lot of redundant association rules, which greatly decrease the accuracy and efficiency of image classification. To relieve the influence of these two problems, this paper proposes a novel approach integrating fuzzy association rules and decision tree to accomplish the task of automatic image annotation. According to the original features with membership functions, the approach first obtains fuzzy feature vectors, which can describe the ambiguity and vagueness of images. Then fuzzy association rules are generated from fuzzy feature vectors with fuzzy support and fuzzy confidence. Fuzzy association rules can capture correlations between low-level visual features and high-level semantic concepts of images. Finally, to tackle the large size of fuzzy association rules base, we adopt decision tree to reduce the unnecessary rules. As a result, the algorithm complexity is decreased to a large extent. We conduct the experiments on two baseline datasets, i.e. Corel5k and IAPR-TC12. The evaluation measures include precision, recall, F-measure and rule number. The experimental results show that our approach performs better than many state-of-the-art automatic image annotation approaches.     

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。     

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。     

基于综合度量的关联规则挖掘算法

从确定性，有用性，简洁性和新奇性4个方面对规则进行综合度量，给出了规则长度和兴趣度约束的定义，在传统算法基础上提出了基于综合度量的关联规则挖掘算法，使关联规则的挖掘质量得到提高。     

数据质量检测规则挖掘方法

数据质量规则是检测数据库质量的关键。为从关系数据库中自动发现数据质量规则,并以其为依据检测错误数据,研究质量规则表示形式及其评估度量,提出以数据项分组及其可信度为依据的最小质量规则计算准则、挖掘算法以及采用质量规则检测错误数据的思路。该数据质量规则形式借鉴关联规则的可信度评估机制、条件函数依赖的表达能力,统一描述函数依赖、条件函数依赖、关联规则等,具有简洁、客观、全面、检测异常数据准确等特性。与相关研究相比,降低挖掘算法的时间复杂度,提高检错率。用实验证明该方法的有效性和正确性。     

Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses

IntroductionAn important quality of association rules is novelty. However, evaluating rule novelty is AI-hard and has been a serious challenge for most data mining systems.ObjectiveIn this paper, we introduce functional novelty, a new non-pairwise approach to evaluating rule novelty. A functionally novel rule is interesting as it suggests previously unknown relations between user hypotheses.MethodsWe developed a novel domain-driven KDD framework for discovering functionally novel association rules. Association rules were mined from cardiovascular data sets. At post-processing, domain knowledge-compliant rules were discovered by applying semantic-based filtering based on UMLS ontology. Their knowledge compliance scores were computed against medical knowledge in Pubmed literature. A cardiologist explored possible relationships between several pairs of unknown hypotheses. The functional novelty of each rule was computed based on its likelihood to mediate these relationships.ResultsHighly interesting rules were successfully discovered. For instance, common rules such as diabetes mellitus?coronary arteriosclerosis was functionally novel as it mediated a rare association between von Willebrand factor and intracardiac thrombus.ConclusionThe proposed post-mining domain-driven rule evaluation technique and measures proved to be useful for estimating candidate functionally novel rules with the results validated by a cardiologist.