毕业设计说明书（论文）中文摘要本文首先简单介绍了数据挖掘的基本理论和基础概念，然后给出了关联规则及模糊理论的基本概念及其发展的基本状况；阐述了Aproiri的基本思想，并提出用隶属度函数对数值型关联规则的模糊化思想。本文重点是做了模糊关联规则方面的研究和实验，同时，为了更加有效的确定隶属度函数，本文还做了基于聚类的隶属度函数确定的研究。首先，本文在对模糊关联经典算法Apriori进行研究学习的基础上，分析优缺点，重点选择了基于改进的AprioriTid的模糊关联算法FAMA进行重点研究。同时，用经典算法Kmeans确定聚类中心，从而获得更加准确的隶属度函数。然后基于模糊关联技术，通过编程设计了一款专用的简易的基于模糊关联技术的挖掘私人银行客户信息的数据挖掘软件。64936

毕业论文关键词  数据挖掘 模糊关联 聚类 隶属度函数

毕业设计说明书（论文）外文摘要

Title Realization of Fuzzy Association Rules

Abstract This paper first introduces the basic theory of data mining and basic concepts, then gives fuzzy association rules and the basic concepts and development of the basic situation; expounded Aproiri the basic idea and proposed using the membership function for quantitative association rules fuzzy thinking. This paper focuses on fuzzy association rules  research and experiments, meanwhile, in order to more effectively determine the membership function, the paper also made to determine the membership function of cluster studies. Firstly, in the classical algorithm Apriori for fuzzy association study of learning, based on the analysis of strengths and weaknesses, focusing on selected based on improved fuzzy association algorithm FAMA AprioriTid focus on research. Meanwhile, with the classic algorithm Kmeans determine the cluster center, to obtain a more accurate membership function. Then, based on fuzzy association technology through programming I designed a simple dedicated private banking customer information data mining software.

Keywords  data mining  fuzzy association  clustering  membership function 

目   次

1 绪论 1

1.1 研究背景及意义 1

1.2 数据挖掘的基本理论 2

1.3 论文的研究内容及组织结构 4

2 模糊关联规则 5

2.1 关联规则的基本理论 5

2.2 布尔型关联规则和Apriori算法 5

2.3 数量型关联规则 8

2.4 模糊关联规则 9

2.5 模糊关联算法(FAMA) 10

2.6 本章小结 12

3 基于聚类的隶属度函数确定的方法研究 12

3.1 聚类技术基本理论 12

3.1.1 基于划分的聚类算法 12

3.1.2