【作者】 陈敏；

【导师】 赵书良；

【作者基本信息】 河北师范大学 ， 计算机科学与技术， 2015， 硕士

【摘要】 关联规则可视化知识发现是一种利用可视化技术从数据中识别有效的、新颖的、潜在的、有用的、可理解的关系模式的非平凡过程。由于人类的大脑具有很强的模式识别能力,因此将知识发现过程结合相应的可视化技术展现可充分发挥人类视觉认知的优势,大大减轻理解负担,从抽象数据中获得新的见解,进而促进知识的转换和创建。过程模型对于项目实施具有重要的指导意义。同时,在关联规则可视化知识发现过程中,关联规则的信息可视化和知识可视化是非常重要的部分。然而,目前缺少对关联规则可视化知识发现过程模型的研究,已有的关联规则可视化方法也存在许多问题,需将知识发现过程模型与好的可视化方法结合以获得更有价值的结果。本文主要做了如下工作:1)提出一种新的更为全面、更加满足实际应用的关联规则可视化知识发现过程模型V-KDDM。模型引入知识管理领域的数据-信息-知识-智慧层次理念,融入人类大脑管理知识的思维模式,便于决策者宏观掌握整个知识发现与数据挖掘实现过程;结合多种可视化技术,有效利用人类视觉系统的优势,逐步实现从数据到信息,从信息到知识,从知识到智慧的转换;将整个V-KDDM过程视为一个人机交互的过程,充分发挥用户的主动性,实现“知识共享”。2)针对现有的关联规则信息可视化形式中,主要面向专家用户而忽略了普通用户的感知与理解能力,以及当规则数量增多时,边和节点的代表元素易发生重叠,缺乏有效的展现形式,降低了可读性等问题,提出了一种新的基于S-C元图形式的一对一、一对多、多对一、多对多的关联规则信息可视化方法。给出了S-C元图的基本定义和以S-C元图展示关联规则的模型以及S-C元图的性质和推导过程。并且进一步在S-C元图关联规则信息可视化方法中加入概念关系,构成Vis-Meta图,利用Vis-Meta图进行关联规则知识可视化。给出了Vis-Meta图的相关定义与关联规则的Vis-Meta图知识可视化方法,定义了关联规则Vis-Meta图知识可视化中的概念关系,并在此基础上给出了关联规则知识可视化相关算法,将隐性知识转换为显性知识。3)设计并实现了关联规则可视化知识发现原型系统。并将某省人口数据应用于原型系统,验证了本文所提出的关联规则信息可视化方法和关联规则知识可视化方法的有效性,同时证明了V-KDDM模型的合理性及其应用价值。更多还原

【Abstract】 Visual knowledge discovery of association rules is a nontrival process of identifying valid, novel, potentially useful, and ultimately understandable patterns using visualization techniques. As the brain of human being has a strong ability of pattern recognition, combining visual knowledge discovery process and visualization technology would be usefull to fulfill the advantage of the human visual cognition, as well as reducing the burden of understanding, giving new insights from the abstract data, and contributing to the knowledge conversion and creation. Process model has an important guiding significance for the implementation of the project. Meanwhile, information visualization and knowledge visualization are very important parts in visual knowledge discovery process of association rules. However, current researches focusing on the visual knowledge discovery process model of association rules are rare, the existing visualization methods of association rules also have many problems. In order to achieve a more valuable result, there is a need to combine knowledge discovery process model with great visualization methods. So this dissertation did the following work:1) Proposing a more practical, more comprehensive application model of visual knowledge discovery process, which is called V-KDDM. By introducing the theory of data-information-knowledge-wisdom hierarchy and integrating the thinking pattern when human brain managing knowledge, it is easier for decision-makers to grasp a macro of the whole knowledge discovery and data mining implementation process; and by combining with a variety of visualization techniques, using the advantage of human’s visual system, the model gradually implements transformation from data to information, from information to knowledge, and from knowledge to wisdom; and by viewing the entire V-KDDM model as a process of human interaction and giving fully play to the initiative of the user, ultimately achieve knowledge sharing.2) The traditional association rule information visualization approaches are mainly orienting to expert users while ignoring the normal user’s perception and comprehension ability. Meanwhile when the number of rules increasing, edges and nodes prone to overlap, as well as result in reducing the performance and readability of rule representation. This dissertation proposes a novel form of information visualization method based on S-C metagraph to show one to one, one to many, many-to-many association rules. This dissertation gives the basic definition of S-C metagraph and the model showing association rules using S-C metagraph, also gives the properties and derivation process of S-C metagraph for visualizing association rules. Furthermore, the concept relationship is added to the S-C metagraph based association rule information visualization method and constructe Vis-Meta graph, which is used for association rule knowledge visualization. This dissertation gives the relevant definitions of Vis-Meta graph and Vis-Meta graph knowledge visualization method of association rules, difines the conceptual relationship in Vis-Meta graph for knowledge visualization, and gives the knowledge visualization algorithms based on those, which converting tacit knowledge to explicit knowledge.3) Designing and achieving the prototype system of visual knowledge discovery of association rules. By applying demographic data of a province to the prototype system, the effectiveness of the association rule information visualization method and knowledge visualization method are verified, and the values of V-KDDM model are also proved.更多还原