关联规则向量化挖掘算法及其在车险精算中的应用

本文首先回顾了关联规则的基本概念和传统的Apriori算法，然后利用关联规则的数据库是布尔型数据库的特点，在计算关联规则的支持度和置信度的时候引进向量数乘和向量内积的概念，得到关联规则向量化挖掘算法ARVDA，避免全数据库逐条记录模式匹配和属性分层，提高算法的速度．最后，本文利用提升度量关联规则的重要性，采用单独追踪和对比分析方法，衡量车险精算中风险因子的有效性．结果表明把车辆使用性质作为车险定价的分级因素是比较合理的，对于非运营车辆需要合理的费用附加．     

面向客户群的服务方案配置规则挖掘方法

产品/服务配置规则获取的主要方式是用数据挖掘技术从设计实例数据库中提取.客户化服务方案配置规则为服务功能需求和方案特征间的关联规则.考虑到常用关联规则挖掘算法Apriori具有运算复杂的缺点,提出基于PIETM(Principle of Inclusion—Exclusion and Transaction Mapping)算法的配置规则挖掘方法,考虑置信度和有趣度指标,提取强关联规则.针对配置实例数据库数据量较大时,配置规则挖掘的效率会降低且会产生大量冗余规则的问题,采用二元语义模型表达定性的服务功能需求,将同类客户群的功能需求进行合并,替换多样化的功能需求,减少规则的冗余.最后以一工程机械制造企业服务方案配置规则挖掘为例,验证了所提方法的有效性.     

考虑动态加权A星算法的预翻箱作业优化

为改善堆场作业效率，提出考虑堆场翻箱位规则的集装箱预翻箱作业优化模型并使用动态加权A星算法进行求解，进而确定满足作业规则限制的最优移动序列。基于集装箱装船过程的预翻箱作业，将预翻箱作业过程的贝位布局抽象为A星算法的状态节点，以错误堆叠箱和翻箱迭代深度计算总成本函数，引入动态加权因子控制翻箱迭代方向，提出新颖的搜索分支规则选择有效分支进行搜索。通过上海港码头和相关文献的算例进行实验，结果表明，设计的动态加权启发式函数能够有效改善寻优过程中跳出局部最优解的能力，验证了考虑堆场运作规则-翻箱位规则的算法有效性和稳定性。研究成果能够普遍适用于作业工艺和贝位布局不同的集装箱码头预翻箱作业，能够为堆场运作优化提供决策参考。     

基于关联规则的SNS网站个性化推荐研究

以关联规则为理论基础,针对社会性网络服务(Social Networking Services,SNS)网站的个性化推荐问题进行研究.在系统总结个性化推荐方法基础上,详细给出基于关联规则的个性化推荐过程,综合运用可信度和作用度两个指标更好地对生成的关联规则进行评价.并采用SNS网站实际运营数据有效实现个性化推荐,对个性化推荐技术在SNS网站的实际应用价值进行拓展分析.     

基于多维模糊关联规则的道路交通阻断分析方法

交通阻断成因复杂,与气象环境、道路线形、车辆状态以及交通环境等多因素相关。由于缺乏对造成交通阻断相关因素间潜在关联的研究,交通阻断管控一直是公路管理,特别是高速公路管理的难点。本文提出了一种基于多维模糊关联规则的道路交通阻断分析方法,发掘交通阻断的潜在规律和各因素间的关联关系。首先在国家现有相关划分体系和大量交通阻断(事件)案例的基础上,根据道路管理实际需求,建立了交通阻断多维属性模型,然后利用基于FCM的模糊关联规则,挖掘阻断因素的多维属性的依存关系,得到面向道路交通阻断分析的多维模糊关联规则。通过研究成果的实践应用,证明关联规则可以为道路交通阻断预防和管理提供有效支持,在道路交通阻断分析领域有着良好的应用前景。     

基于对偶犹豫模糊语言变量的多属性决策方法

首先定义了对偶犹豫模糊语言变量,然后给出其运算规则、得分值函数、精确值函数、比较规则以及对偶犹豫模糊语言变量的加权算术平均算子、有序加权算术平均算子和混合平均算子。针对属性值为对偶犹豫模糊语言变量的多属性决策问题,提出了一种基于对偶犹豫模糊语言变量集结算子的多属性决策方法。最后,结合国家电网公司合作单位选择问题,验证了该方法的有效性和可行性。     

基于隶属度与非隶属度交叉影响的直觉模糊集运算法则及其应用

考虑到不同直觉模糊集的隶属度与非隶属度之间可能存在着某些关联和相互影响,本文提出了直觉模糊集上的改进的加法运算、数乘运算、乘积运算和幂运算.在这些运算的基础上,我们重新给出了直觉模糊加权算术平均算子、直觉模糊有序加权算术平均算子、直觉模糊加权几何平均算子和直觉模糊有序加权几何平均算子的表达式,并研究了他们的一些性质.最后通过实例,说明了新的集成算子在决策应用中的有效性.     

轨形Riemann面的Gromov-Witten不变量沿光滑点的加权涨开公式

本文考虑当一个紧辛轨形(orbifold)Riemann面(X,ω)沿着光滑点作加权涨开时,它的轨形Gromov-Witten不变量的变化情形和辛一致规则(uniruledness)性质的变化情形;证明了如下的结果:α_1,...,α_m~X _g,A=p*α_1,...,p*α_m,■,α_1,...,α_m,[pt]~X _g,A=I_A·■.第一个公式表明,当(X,ω)是辛一致规则的(uniruled)时,它的沿光滑点的加权涨开■也是辛一致规则的.     

关联规则及其在石油期货价格预测中的应用研究

针对Apriori算法及其变种的不足,提出了一种基于支持度计数矩阵和事务数据库布尔矩阵的新算法,利用此算法挖掘石油期货价格历史数据中的频繁项集,根据给定的最小支持度和最小置信度从挖掘结果产生关联规则,对关联规则在石油期货价格预测中的应用进行了探索,并提出了进一步的研究方向.     

400 GeV/c PP碰撞的阶乘关联子数据 *

用400GeV/c质子-质子(PP)碰撞产生的带电粒子赝快度分布的实验数据,计算了阶乘关联子和累积关联子.结果表明阶乘关联子随着关联距离的减小而增加,与赝快度的分辨率无关.两粒子关联长度近似与平均多重数无关.对阶乘关联子与阶乘矩之间存在的相加规则进行了实验检验.     

Interpretation of association rules in multi-tier structures

Dealing with the large amount of data resulting from association rule mining is a big challenge. The essential issue is how to provide efficient methods for summarizing and representing meaningful discovered knowledge from databases. This paper presents a new approach called multi-tier granule mining to improve the performance of association rule mining. Rather than using patterns, it uses granules to represent knowledge that is implicitly contained in relational databases. This approach also uses multi-tier structures and association mappings to interpret association rules in terms of granules. Consequently, association rules can be quickly assessed and meaningless association rules can be justified according to these association mappings. The experimental results indicate that the proposed approach is promising.     

具有模糊属性的关联产生式发掘算法及应用

本文通过不确定性推理的分析，提出了模糊关联的概念，用模糊概念表示事务数据之间的关联关系，研究了模糊关联的性质，给出了模糊关联产生式的发掘算法及应用的实例．     

基于多层次模糊关联算法的通用航空产业知识挖掘研究

为挖掘通用航空产业领域知识的类型与发展规律,明确产业规划、推动产业健康发展,利用采集的3869份通用航空产业主题相关的网页资料,根据主题信息的关键词对通用航空产业领域知识进行分类,并基于多层次模糊关联算法进行知识挖掘分析.结果表明,通用航空产业领域知识中的通用航空产业主体与通用航空产业产品存在着紧密的联系和规则,而且采...     

基于协方差的FP-Growth算法在ERP中的研究与应用

文章简要介绍了关联规则挖掘技术,通过对经典算法FP-Growth在某鞋业ERP系统中的应用分析,发现了FP-Growth算法挖掘的部分数据无用的缺陷,提出了一种增加协方差兴趣度阈值的改进算法—CovFP-Growth算法.该算法采用协方差的概念,能够更准确地挖掘出交易集中不同产品间的紧密相关性,减少产生的无用规则,为ERP的产品预测计划决策提供了良好的理论依据和实现方法.     

Selective association rule generation

Mining association rules is a popular and well researched method for discovering interesting relations between variables in large databases. A practical problem is that at medium to low support values often a large number of frequent itemsets and an even larger number of association rules are found in a database. A widely used approach is to gradually increase minimum support and minimum confidence or to filter the found rules using increasingly strict constraints on additional measures of interestingness until the set of rules found is reduced to a manageable size. In this paper we describe a different approach which is based on the idea to first define a set of “interesting” itemsets (e.g., by a mixture of mining and expert knowledge) and then, in a second step to selectively generate rules for only these itemsets. The main advantage of this approach over increasing thresholds or filtering rules is that the number of rules found is significantly reduced while at the same time it is not necessary to increase the support and confidence thresholds which might lead to missing important information in the database.     

A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling

This paper considers the outpatient no-show problem faced by a rural free clinic located in the south-eastern United States. Using data mining and simulation techniques, we develop sequencing schemes for patients, in order to optimize a combination of performance measures used at the clinic. We utilize association rule mining (ARM) to build a model for predicting patient no-shows; and then use a set covering optimization method to derive three manageable sets of rules for patient sequencing. Simulation is used to determine the optimal number of patients and to evaluate the models. The ARM technique presented here results in significant improvements over models that do not employ rules, supporting the conjecture that, when dealing with noisy data such as in an outpatient clinic, extracting partial patterns, as is done by ARM, can be of significant value for simulation modelling.     

Using data mining to identify structural rules in proteins

The progress in bioinformatics and biotechnology area has generated a huge amount of sequences that requires a detailed analysis. There are several data mining techniques that can be used to discovery patterns in large databases. This paper describes the development of a tool/methodology to extract hydrophobicity patterns/profiles that archives a specific secondary structure in proteins. The results indicate that association rules can be efficient method to investigate this kind of problem. This work contributes for two areas: prediction of protein structure and protein folding.     

TRACTABILITY OF MULTIVARIATE INTEGRATION PROBLEM FOR PERIODIC CONTINUOUS FUNCTIONS

The authors study the tractability and strong tractability of a multivariate integration problem in the worst case setting for weighted l-periodic continuous functions spaces of d coordinates with absolutely convergent Fourier series.The authors reduce the initial error by a factor ε for functions from the unit ball of the weighted periodic contin- uous functions spaces.Tractability is the minimal number of function samples required to solve the problem in polynomial in ε~(-1)and d.and the strong tractability is the pres- ence of only a polynomial dependence in ε.This problem has been recently studied for quasi-Monte Carlo quadrature rules.quadrature rules with non-negative coefficients. and rules for which all quadrature weights are arbitrary for weighted Korobov spaces of smooth periodic functions of d variables.The authors show that the tractability and strong tractability of a multivariate integration problem in worst case setting hold for the weighted periodic continuous functions spaces with absolutely convergent Fourier series under the same assumptions as in Ref.[14]on the weights of the Korobov space for quasi-Monte Carlo rules and rules for which all quadrature weights are non-negative.The arguments are not constructive.