【作者】 郎瑾；

【导师】 王保保；

【作者基本信息】 西安电子科技大学 ， 计算机应用技术， 2005， 硕士

【摘要】 随着数据库与互联网技术的发展，人们每天可以获得的数据及信息量呈指数级增长。为解决随之带来的如何从浩瀚的数据海洋中提取有用的知识以便为决策者提供决策支持的问题，数据挖掘技术应运而生。关联规则是数据库中某些特定事件一起发生的概率的简单陈述。关联规则挖掘就是利用特定方法发掘数据库中潜藏的关联规则的过程。目前，关联规则挖掘已经成为数据挖掘领域重要的研究方向之一。本文主要研究了关联规则挖掘中的若干技术，首先介绍了数据挖掘及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了分类探讨，并深入分析了其中的几种典型算法。此外还研究比较了关联规则挖掘结果的几种可视化表示技术，在这部分内容之后讨论的是关联规则挖掘中的隐私保护问题。本文的第五章给出了一种基于Apriori的改进算法，并将该算法进行扩展以用于类别关联规则的挖掘。最后总结全文，并展望了进一步研究的方向。更多还原

【Abstract】 With the development of database and Internet technology, the volumes of data and information which can be obtained increase at tbe speed of exponent. Data mining comes up to solve the problem that how to distill the useful knowledge which can be used for decision supporting from masses of data. Association rule is a simple statement about the cooccurrence probability of some certain events in database. Association rule mining aimed to find the associated relationship of a great deal of item sets in the database. At present, association rule mining has become an important branch of data mining research.This thesis emphasizes on several techniques of association rule mining. At first, the background knowledge of data mining and association rule mining are introduced briefly. After that, some kinds of common used association rule mining algorithms are discussed respectively; among them certain typical algorithms are analyzed and compared thoroughly. Furthermore, the results visualization techniques of association rule mining are studied. The followed content is the discussing of privacy preserving techniques. Then, this thesis presents an improved algorithm based on classic Apriori algorithm, and extends it for class association rule mining. In the last part of the thesis, we give the conclusion and prospect of data mining research.更多还原