分布式数据库中关联规则挖掘的研究

在数据挖掘研究中,关联规则挖掘作为数据挖掘研究中的一个重要部分,引起越来越多的关注。因此,主要研究关联规则挖掘,首先介绍关联规则挖掘的一些基础知识、概念描述等,然后对关联规则挖掘的常用算法进行分类探讨,最后分析其中的几种典型算法。     

一种基于目标属性的关联规则挖掘算法的研究

提出一种基于目标属性的关联规则挖掘算法,该算法对于不同目标属性的关联规则挖掘是相互独立的,虽然会产生重复工作,但是在并行计算以后效率得到了大大的提高.经过实验分析,对于同样的问题,采用该算法并引入并行计算后,效率要比采用间接挖掘方式的Apriori算法高得多.     

一种基于索引数组的频繁项集高效挖掘算法

为改进基于数据库垂直表示的频繁项集挖掘算法的性能,给出了用索引数组方法来改进计算性能的思路.提出了索引数组的概念及其计算方法,并提出了一种新的高效的频繁项集挖掘算法Index-FIMiner.该算法大大减少了不必要的tidset求交及相应的频繁性判断操作,同时也论证了代表项可直接与其包含索引中的所有项集的组合进行连接,这些结果项集的支持度均与代表项的支持度相等,从而降低了这些频繁项集的处理代价,提高了算法的性能.实验结果表明,Index-FIMiner算法具有较高的挖掘效率.     

关联规则挖掘算法综述

介绍了关联规则挖掘算法的基本原理，并按照挖掘中涉及到的变量数目（维数）、数据的抽象层次和处理变量的类别（布尔型和数值型），依次对关联规则挖掘算法的研究进行综述，并对一些典型的算法进行分析和比较，最后展望了关联规则挖掘算法的研究方向。     

基于压缩事务矩阵相乘的Apriori改进算法

Apriori算法是当前使用最广泛的关联规则挖掘方法中最为经典的算法之一;但是该算法需要反复的扫描数据库,在I/O上花消很大,并且在得到频繁-2项集的过程中会产生庞大的候选-2项集,其次在筛选得到频繁-k项集时,并没排除那些不应该参组合的元素,而导致该算法效率很低,针对上面影响计算效率的三个方面提出基于压缩事务矩阵相乘得到频繁项目集的算法,只需一次扫描数据库,经过压缩处理产生产生事务矩阵,通过矩阵间运算得到频繁项目集,有效提高了关联规则的挖掘效率。     

数据挖掘技术与关联规则挖掘算法研究

随着人们对信息数据量的急速增长从而数据挖掘技术也随之应运而生,这使得人们对知识与信息的渴求得到了进一步满足。对于如何才能快速高效的获取知识,对于信息处理技术来说已经成为当前热门的研究课题。审视当前对于关联规则的研究现状,针对关联研究的现状,分析实际问题对于关联规则总结出一种新的研究方式,结论为关联规则算法在今后的出路和进一步的研究上指明了方向。研究过程中通过对文献的查询分析和比较分析两种方法,进一步阐述对典型关联产生影响的各种方法,其中最为重要的是把核心Apriori算法作为一个研究的基点。     

基于Apriori算法的课程成绩关联规则挖掘研究

数据挖掘中的关联规则挖掘能够发现大量数据中项集之间有趣的关联或相关联系,采用关联规则的Apriori算法和改进后的Apriori算法对郑州职业技术学院学生各门课程成绩进行分析,从而发现课程之间的联系和基础课程对专业课程的影响,为教务昔理部门安排课程提供参考。     

数据库中广义模糊关联规则的挖掘

引入了广义模糊关联规则的概念,给出挖掘规则的计算方法,用来进行数据挖掘,以找出隐藏在数据库当中那些有用的而未被发现的知识。     

正负关联规则挖掘的研究

传统的基于支持度-置信度框架的关联规则挖掘方法可能会产生大量不相关的、甚至是误导的关联规则,同时也不能区分正负关联规则。本文提出了一种评价关联规则的可量化标准,进一步提出一种能同时挖掘正负关联规则的框架,实验证明该方法是有效的。     

改进Apriori算法对试车台监测数据的关联挖掘

为发现某试车台中流量、压力等数据之间的关联关系,引入关联规则对监测数据进行挖掘,得到对提高试车安全监测性能具有较高的置信度和支持度的关联规则。提出改进的Apriori算法,对某型航空部件试车台试车过程的数据进行挖掘,与原方法相比,该方法效率高,在置信度和支持度相同的情况下,可以有效降低运算时间。     

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%. The pruning strategy was adopted based on evaluation measures. Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values. To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects. The evaluation showed that the results of our proposal are promising. Practitioners and developers can utilize these rules for defect prediction during early software development.     

数据挖掘在包装产品网络营销中的应用

分析了包装产品网络营销中原始数据的特点与分类的基础上,开发了基于数据挖掘的包装产品网络营销系统。     

个性化包装设计中关联规则挖掘技术的应用

从个性化包装产生的原因出发,分析了个性化包装的特点及个性化包装设计的重要性,阐述了基于关联规则挖掘的个性化包装设计的基本方法,详细说明了关联规则挖掘在个性化包装中应用的关键技术,提出了一种基于关联规则挖掘的个性化包装设计模型,并给出了其关键步骤的实现方法。     

A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment

In the era of digitalisation, e-commerce retail sites have become decisive channels for reaching millions of potential customers worldwide. Digital marketing strategies are formulated by the marketing teams in order to increase the traffic on their e-commerce sites, thereby boosting the sales of the products. With the massive amount of data available from the cloud, which were conventionally made with a high degree of intuition based on decision makers’ knowledge and experience, can now be supported with the application of artificial intelligence techniques. This paper introduces a novel approach in applying the fuzzy association rule mining approach and the fuzzy logic technique, for discovering the factors influencing the pricing decision of products launched in e-commerce retail site, and in formulating flexible, dynamic pricing strategies for each product launched in an e-commerce site. A pricing decision support system for B2B e-commerce retail businesses, namely Smart-Quo, is developed and implemented in a Hong Kong-based B2B e-commerce retail company. A six-month pilot run reveals a significant improvement in terms of the efficiency and effectiveness in making pricing decisions on each product. The case study demonstrates the feasibility and potential benefits of applying artificial intelligence techniques in marketing management in today’s digital age.     

Guiding functional design of mechanical products through rule-based causal behavioural reasoning

The paper presents a behaviour-driven functional (B-FES) modelling framework for functional design of mechanical products based on a rule-based causal behavioural reasoning step to guide the design process. A new representation scheme called rule-based behavioural representation (causal behavioural rules) was developed to facilitate causal behavioural reasoning, with which the interconnected physical behaviours can be reasoned out from a desired function. The behaviour schema was then used to select and arrange embodiments (abstractions of physical artefacts) to develop a set of potential concept variants. The proposed approach was not only useful in the creation of new configurations (combinations) from a library of standard physical behaviours, but also it might be used to generate specifications of new physical behaviours. A design case study of a terminal feeding unit is presented to demonstrate the practicality of the proposed approach.     

关联规则挖掘在企业运作策略调查问卷中的应用

通过分析企业运作策略问卷调查数据,提取出不同的运作策略与绩效间的关联规则。首先介绍了关联规则以及关联规则兴趣度的度量。对于问卷数据,进行了预处理,确定挖掘的数据集及相关属性。然后应用关联规则挖掘工具,挖掘出潜在的有用规则。通过分析关联规则,找出企业运作策略及绩效间的关系,为企业提供决策支持,提高企业的竞争力。     

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN) model is applied for the effective prediction of rainfall data. Moreover, COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning (COBL) with traditional MFO algorithm. Finally, the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model. For demonstrating the improved outcomes of the COMFO-DLRP approach, a sequence of simulations were carried out and the outcomes are assessed under distinct measures. The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.     

结合数据融合和数据挖掘技术的信息智能处理平台

数据融合技术能利用不同传感数据的冗余信息实现互补以提高信息处理的正确性；数据挖掘技术能从大量数据中挖掘和发现有价值和隐含的知识，用于建模和优化。本文研究结合数据融合和数据挖掘技术的信息智能处理平台，阐述了其功能和组成，知识表达和建模、知识推理和决策，并介绍了其在目标检测识别和医疗监护等领域的应用。     

A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0

In Retail 4.0, omni-channels require a seamless and complete integration of all available channels for purchasing. The diversification of channels not only diversifies data sources, but also rapidly generates an enormous amount of data. This highlights a need of big data analytics to extract meaningful knowledge for decision-making. In addition, anticipatory shipping is getting more popular to ensure fast product delivery. The goal is to predict when a customer will make a purchase and then begin shipping the product to the nearest distribution centres before the customer places the orders online. This paper proposes a genetic algorithm (GA)-based optimisation model to support anticipatory shipping. Cloud computing is deployed to store the big data generated from all channels. Cluster-based association rule mining is applied to discover the purchase pattern and predict future purchase in terms of If-Then prediction rules. A modified GA is then used to generate optimal anticipatory shipping plans. Apart from transportation cost and travelling distance, the confidence of prediction rules is also considered in the GA. A number of numerical experiments have been carried out to demonstrate the trade-off of different factors in anticipatory shipping, and the optimisation reliability of the model is verified.