一种基于多值属性的改进Apriori算法

随着大量需要被挖掘的数据变得越来越复杂,多维关联规则已经成为关联规则挖掘中最实用的内容之一.本文主要介绍了在多维关联规则挖掘过程中,针对同一种属性数据出现重复连接的情况,由此而提出的一种解决方案.通过对多值属性信息进行比较,去除重复连接的属性信息,保留有效信息,减少对数据库的扫描.由此对Apriori算法中连接步进行改进,最后通过布尔型关联规则挖掘数据信息并得到结果.相较于Apriori算法,改进算法能更加快速准确地发现知识,缩短挖掘所用的时间.     

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。     

一种基于蝙蝠算法的多目标跟踪数据关联方法

针对多目标跟踪中的数据关联问题,提出了基于蝙蝠算法的数据关联方法。首先将多目标数据关联问题建模成组合优化问题,结合数据关联的特点,对蝙蝠算法的搜索更新规则进行改进,使其可以应用于多目标的数据关联问题,并给出了基于蝙蝠算法的多目标数据关联的详细流程。通过仿真实验和被动声呐实测数据测试表明,基于蝙蝠算法的多目标数据关联方法切实可行并且具有较好的效果。     

数据挖掘中关联规则的频繁项集研究及算法设计

关联规则挖掘其主要研究目的是从大型数据集中发现隐藏的、有趣的、属性间存在的规律与数据间的联系。关联规则挖掘算法主要目的是从事务数据集项间挖掘出有意义的关联关系。Apriori算法是关联规则挖掘算法中最经典的方法。由Apriori算法产生的候选项集仍是巨量的。通过对Apriori算法中的候选项集支持频度的深入研究总结五条规律,并将这五条规律应用到Apriori算法中。     

基于关联规则的恒星光谱数据相关性分析

利用关联规则作为恒星光谱数据相关性分析方法,以VC 和Oracle9i作为开发工具,设计并以程序实现了基于关联规则的恒星光谱数据相关性分析系统,给出了其软件体系结构和模块功能,并对恒星光谱数据的预处理、取样技术、背景知识模板、频繁模式集挖掘算法等关键技术进行了详细描述.最后,通过系统运行结果表明,利用关联规则来描述、分析光谱数据特征和物理化学性质之间存在的相关性是可行的和有价值的.     

基于关联规则的多冷水机组系统负荷优化分配

本文以多冷水机组系统为研究对象,对系统采集的大量历史运行数据进行数据处理与分析,采用Apriori频繁项集算法,通过分级挖掘,挖掘在不同运行工况下各台冷水机组运行参数与最小运行能耗之间的关联规则,并以机组运行总能耗最小为目标,提出了一种将粒子群算法与关联规则结合的负荷分配优化方法.仿真验证结果表明:该方法通过优化冷水机...     

基于改进模拟退火遗传算法的自动化立体仓库 货位优化

针对自动化立体仓库出入库作业量大、品类复杂等特点,对仓库货位展开优化研究,遵循周转率原则、货架稳定原则、关联原则等,构建了提高出库效率、提高货架稳定性、增强货物相关性的货位优化模型。使用传统模拟退火算法、遗传算法与改进模拟退火遗传算法求解模型,得出货位优化结果。对比分析结果证明了在解决货位优化问题时,改进算法比传统基础算法更加有效,能更好地改善自动化立体仓库空间使用率低、拣选效率低、货位摆放混乱的现状。     

基于动态模糊关联规则推理的光网络故障管理(英文)

当代光网络趋于复杂化,一个故障将引发多个告警事件。同时,同一告警可能由不同的故障导致。本文研究了基于数据挖掘的光网络告警相关性分析,我们从动态网络资源与设备中挖掘关联规则,充分利用和维护原有规则知识,使网络结构和规则库都能快速更新,并提出了新型的动态模糊关联规则挖掘算法IDFARM。同时运用模糊逻辑将数值型告警属性转化为逻辑语言变量,当网络中有新的未知告警发生时,我们对模糊关联规则运用模糊推理来进行故障诊断,这将缩短网络恢复时间,有利于提高光网络故障管理性能。仿真实验验证了文章算法的正确性和有效性。     

数据分类挖掘及分析系统的实现

数据挖掘（DataMiningDM）技术对知识获得的重要性;较详细的分在数据分类挖掘中，ID3的算法流程及数学模型；并介绍了该方法在职业教育中的分析应用，为专业建设提供决策依据。     

改进的多岛遗传算法在动力总成悬置系统优化设计中的应用

针对多岛遗传算法（Multi-Island GA）的缺点,通过动态调节其交叉和变异概率来防止算法的早熟以及提高搜索效率,并结合模拟退火法的局部寻优能力强的优点,形成了求解效率更高的改进的多岛遗传算法,并将其应用于动力总成悬置系统的优化设计中。并通过仿真分析和实验验证了优化效果,该改进的算法可以有效应用在悬置系统的优化设计中。     

CARM: Context Based Association Rule Mining for Conventional Data

This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations. Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique. The context, in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data. Conditional Probability Increment Ratio (CPIR) is also added in the proposed algorithm that was not used in CBPNARM. The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use. Also, the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM. The rules created by the proposed algorithm are more meaningful, significant, relevant and insightful. The accuracy of the proposed algorithm is compared with the Apriori, PNARM and CBPNARM algorithms. The results demonstrated enhanced accuracy, relevance and timeliness.     

Evolutionary association rules for total ozone content modeling from satellite observations

In this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quantitative Association Rules) that extends a recently published algorithm by the authors and we describe its application to a problem of Total Ozone Content (TOC) modeling in the Iberian Peninsula. We use TOC data from the Total Ozone Mapping Spectrometer (TOMS) on board the NASA Nimbus-7 satellite measured at three locations (Lisbon, Madrid and Murcia) of the Iberian Peninsula. As prediction variables for the association rules we consider several meteorological variables, such as Outgoing Long-wave Radiation (OLR), Temperature at 50 hPa level, Tropopause height, and wind vertical velocity component at 200 hPa. We show that the best association rules obtained by EQAR are able to accurate modeling the TOC data in the three locations considered, providing results which agree to previous works in the literature.     

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to find the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum.     

Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is well-performed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and member-ship functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.     

基于情境—需求本体的客户定制需求挖掘

目的 实现客户定制需求精准全面的自动获取.方法 以人机系统设计理论为基础,构建考虑客户情境信息的情境—需求本体模型并运用Apriori算法挖掘两者之间的映射规则.结论 由客户情境信息映射到客户定制需求信息的关联规则,可以实现根据客户情境信息推导出对应的定制需求信息.运用情境—需求本体模型和关联规则挖掘算法可以实现由客户情境信息自动获取规范化的客户定制需求信息,更符合人机系统设计理论.     

Configuration of cellular manufacturing systems using association rule induction

A cell-formation approach based on association rule induction is developed to find the effective configurations for cellular manufacturing systems. To gain the benefits of flexibility and efficiency, the manufacturing system is decomposed into several manageable subsystems by categorizing similar parts into part families and disparate machines into cells. It is advantageous to find the important associations among machines such that the occurrence of some machines in a machine cell will cause the occurrence of other machines in the same cell. Relationships among machines can be found from the process database by inducting association rules. By applying association rules to cell-formation problems, certain sets of machines (machine groups) that frequently process some parts together can be inducted. A data-mining technique referred to as association rule induction is used herein to find the association rules among machines from the process database. Seventeen data sets of various size and complexity were used to evaluate the effectiveness of the proposed cell-formation algorithm based on association rule induction. The performance of the proposed approach is compared with several existing techniques. From the computational results, the proposed approach shows its ability to find quality solutions.     

Learning dispatching rules using random forest in flexible job shop scheduling problems

In this paper, we address the flexible job-shop scheduling problem (FJSP) with release times for minimising the total weighted tardiness by learning dispatching rules from schedules. We propose a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) in order to extract dispatching rules from the best schedules. RANFORS consists of three phases: schedule generation, rule learning with data transformation, and rule improvement with discretisation. In the schedule generation phase, we present three solution approaches that are widely used to solve FJSPs. Based on the best schedules among them, the rule learning with data transformation phase converts them into training data with constructed attributes and generates a dispatching rule with inductive learning. Finally, the rule improvement with discretisation improves dispatching rules with a genetic algorithm by discretising continuous attributes and changing parameters for random forest with the aim of minimising the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach and the results showed that it outperforms the existing dispatching rules. Moreover, compared with the other decision-tree-based algorithms, the proposed algorithm is effective in terms of extracting scheduling insights from a set of rules.     

A GA-based approach to machining operation sequencing for prismatic parts

This paper presents a study of using a genetic algorithm (GA) method to select the machining operation sequence for prismatic parts. Four types of process planning rules including precedence rules, clustering rules, adjacent order rules and optimization rules are considered and are encompassed quantitatively in the fitness calculations for alternative operation sequences. The impact of variations of the rules on the result of operation sequencing and that of GA parameters on the solution efficiency are discussed through analysis of examples and experiments. The proposed genetic algorithm proves effective for machining operation sequencing of prismatic parts, by incorporating various production environment considerations into process planning.