东南大学学报（自然科学版）2001，Vol.31Issue(2)：31-34,4.
一种基于可信度最优的数量关联规则挖掘算法
An Algorithm for Mining Optimized Confidence Quantitative Association Rules
摘要
Abstract
This paper discusses the problem of discretization for continuousattributes and describes a method for discretization in the processing of mining quantitative association rules, including quantitative ranges partitioning and sampling to a huge database. An algorithm for mining optimized confidence quantitative association rules is presented. In the algorithm, the equi-depth partitioning is used to discrete for continuous attributes and a technique of handing convex hulls is used to compute optimized confidence quantitative association ranges. Given a huge database, we address the problem of finding association rules for numeric attributes, such as (A∈[v1,v2])C, in which C is boolean attribute. Our goal is to realize a system that finds an appropriate range automatically. We use the algorithms to analyse the buying and selling of stocks, finding association rules between stock price and fluctuation of price. The experiment states clearly that the algorithms are correct.关键词
数量关联规则/数据挖掘/连续属性离散化分类
信息技术与安全科学引用本文复制引用
吉根林,孙志挥..一种基于可信度最优的数量关联规则挖掘算法[J].东南大学学报（自然科学版）,2001,31(2):31-34,4.基金项目
国家自然科学基金资助项目(79970092). （79970092）
