自动化学报2009，Vol.35Issue(8)：1071-1079,9.DOI:10.3724/SP.J.1004.2009.01071
关系数据库中知识发现的一种粒计算方法
A Granular Computing Approach to Knowledge Discovery in Relational Databases
摘要
Abstract
The main objective of this paper is to present granular computing algorithms of finding association rules with different levels of granularity from relational databases or information tables. Firstly, based on the partition model of granular computing, a framework for knowledge discovery in relational databases was introduced. Secondly, referring to granular computing, the algorithms for generating frequent k-itemsets were proposed. Finally, the proposed algorithms were illustrated with a real example and tested on two data sets under different supports. Experiment results show that the algorithms are effective and feasible. Moreover, the meanings of mining association rules based on granular computing are clearly understandable.关键词
Granular computing/information granule/knowledge discovery/association ruleKey words
Granular computing/information granule/knowledge discovery/association rule分类
信息技术与安全科学引用本文复制引用
邱桃荣,刘清,黄厚宽..关系数据库中知识发现的一种粒计算方法[J].自动化学报,2009,35(8):1071-1079,9.基金项目
National Natural Science Foundation of China (60173054) and the Key Project of Jiangxi Province in China (20061B01002) （60173054）
