Generating Rare Association Rules Using the Minimal Rare Itemsets Family

Rare association rules correspond to rare, or infrequent, itemsets, as opposed to frequent ones that are targeted by conventional pattern miners. Rare rules reflect regularities of local, rather than global, scope that can nevertheless provide valuable insights to an expert, especially in areas such as genetics and medical diagnosis where some specific deviations/illnesses occur only in a small number of cases. The work presented here is motivated by the long-standing open question of efficiently mining strong rare rules, i.e., rules with high confidence and low support. We also propose an efficient solution for finding the set of minimal rare itemsets. This set serves as a basis for generating rare association rules.     

超声波协同超微粉技术辅助酸法提取黑木耳果胶

探讨了超声波协同超微粉技术辅助酸法提取黑木耳果胶的工艺条件。分别考察了超声波辐射强度、液料比、温度、pH值及提取时间等因素对果胶得率的影响,并以此为基础用正交法优化提取黑木耳果胶的工艺参数。结果表明,采用此法从黑木耳中提取果胶的适宜工艺条件为超声波辐射强度70 W/kg、液料质量比40∶1、温度70℃、pH值3.0、提取时间60 mim。在此条件下,木耳果胶得率可达35.45%,与其他方法相比,果胶得率最高。     

批量准则下的控制图在完全检验中的最优设计

为了帮助企业提高产品质量,减少生产成本,对于对产品采用完全检验的企业,提出了在批量准则下,建立生产过程的期望质量损失成本最优模型,并进行了大量的数值实验,结果表明该准则下的期望质量损失成本总体上优于其他准则.     

基于关联规则挖掘的课程相关性研究与应用

应用关联规则挖掘对高校课程相关性进行了研究．将某高校的毕业生成绩数据库经过预处理之后,采用不设定成绩界限的方法,用改进的Apriori算法进行挖掘．不仅能挖掘出成绩为优时的课程相关规则,还能发现若某些课程成绩差,则其他课程成绩也为差的规则,可以为学分制体系下学生选课和管理者进行决策等提供参考．     

基于关联规则的数据挖掘技术在中医诊断中的应用

以肝病的中医诊断为例,研究了基于关联规则的数据挖掘技术在中医诊断中的应用,探讨了"理-法-方-药"之间的互动规律,为中医临床治疗、中医教学及中成药的研制提供了参考.     

关联规则在图书管理中的应用

关联规则主要用来发现数据库中存在的频繁项集.研究关联规则Apriori算法,并利用该数据挖掘技术对高校图书馆管理信息系统中的数据进行挖掘研究,并将挖掘到的规则结果应用到高校图书馆公共检索系统中,达到优化公共检索系统并且向读者提供多元化信息检索服务的目的.     

工房色彩设计指导原则初探

工房是满足产品加工制造、生产工艺布局需要的建筑物,一般不可能依赖建筑造型本身突出个性,而绝大部分情况下要采用建筑色彩来表现工房的统一性或个性。城市工房由于城市发展、城市主导产业的转型等因素面临着和城市中其它建筑、外部环境不和谐、甚至对立的矛盾,改造中色彩的有效配置不仅美化了环境,而且可以创造舒适的工作氛围,激发劳动热情,提高产品质量和生产效率,降低劳动强度,减少生产事故,保障工人的健康与安全,实现科学化管理。     

CA与生命游戏演化的组合模型*

基于摩尔奇偶型CA,提出一种结合生命游戏的演化模型.首先设置初始构型进行摩尔奇偶型规则演化,再引入生命游戏规则,按游戏规则演化;最后依此重复摩尔奇偶规则和生命游戏规则,直到演化整个元胞空间.证实在摩尔奇偶型CA的基础上,结合生命游戏在很大程度上减小了演化过程对初始构型的依赖,出现更为丰富的图元以及基于图元和演化框架的动态图案,且演化过程仍然符合摩尔奇偶型CA的演化规律.结合模型可以避免演化过度依赖初始构型以及演化过程的单调性,而且组合演化模型可以应用于图案生成,生成保持风格的规则几何纹理图案,丰富图案的生成方法.     

一种基于生成树的分类去除关联规则冗余方法

在信息及数据爆炸的时代,冗余问题已经成为数据挖掘者获得知识的重大障碍,而目前解决的方法会导致关联规则的不完整性。基于此,文中引入了有向超图表示关联规则,重定义了邻接矩阵,介绍了冗余规则分类处理思想,将冗余规则分为从属规则和重复路径规则,通过VB编程去除了从属规则冗余,以及利用生成树算法去除了重复路径规则冗余。实验结果证明,此方法创新性地结合了图论中有向超图、生成树与关联规则的知识,维护了关联规则的完整性和准确性,同时去除了全部冗余规则。     

Artificial intelligence for BIM content management and delivery: Case study of association rule mining for construction detailing

The proliferation of Building Information Modeling (BIM) applications, in tandem with the extensive variation of building products, pose new demands on design and engineering firms to efficiently manage and reuse BIM content (i.e., data-rich parametric model objects and assembly details). Tasks such as classifying BIM objects, indexing them with meta-data (e.g., category), and searching digital libraries to load objects into models still plague practice with inefficient manual workflows. This research aims to improve the productivity of BIM content management and retrieval by developing an AI-backed BIM content recommender system. Using data from a case-study firm, this research extracted content from over 30,000 technical BIM views (e.g., plans, sections, details) in historical projects to build an unsupervised machine-learning prototype with association rule mining. This prototype explicated the strength of relationships among co-occurring BIM objects. Using this prototype as the backbone AI-engine in live BIM sessions, this research developed a context-aware recommender system that dynamically provides BIM users with a set of objects associable with their modeling context (e.g., type of view, existing objects in the model) and human–computer interactions (e.g., objects selected by the user). By mining association data from hundreds of historical projects, this development marks a departure from the existing prototypes that rely on explicit coding, recurring user input, or subjective ratings to recommend BIM content to users. The simulation and experimental implementation of this recommender system yielded high efficacy in predicting content needs and achieved significant savings in the time spent on conventional BIM workflows.